EAI Endorsed Transactions on Pervasive Health and Technology

COVID-19 and Suicide Tendency: Prediction and Risk Factor Analysis Using Machine Learning and Explainable AI

Mon, 18 Mar 2024 00:00:00 +0000

INTRODUCTION: Pandemics and epidemics have frequently led to a significant increase in the suicide rate in affected regions. However, these unnecessary deaths can be prevented by identifying the risk factors and intervening earlier with those at risk. Numerous empirical studies have exhaustively documented multiple suicide risk factors. In addition, many evidence-based approaches have employed machine learning models to diagnose vulnerable groups, a task that would otherwise be challenging if only human cognition were employed. To date, to the best of our knowledge, no research has been conducted on COVID-19-related suicide prediction.

OBJECTIVES: This research, aims to develop a machine-learning model capable of identifying individuals who are contemplating suicide due to COVID-19-related complexities and assessing the potential risk factors.

METHODS: We trained a gradient-boosting model based on tree-based learners on 10067 data consisting of 76 features, which were primarily responses to socio-demographic, behavioural, and psychological questions about COVID-19 and suicidal behaviours.

RESULTS: The final model predicted individuals at risk with an auROC score of 0.77 and a 95% confidence interval of 0.77 to 0.88. The optimal cutoff produced a sensitivity of 31.37 percent and a specificity of 82.35 percent in predicting suicidal tendencies. However, the auPRC was only 0.26, with a 95 percent confidence interval of 0.13 to 0.38, as the class distribution was extremely unbalanced. Consequently, the scores for precision and recall were 0.35 and 0.31, respectively.

CONCLUSION: We investigated the risk factors, the majority of which were associated with sleeping difficulties, fear of COVID-19, social interactions, and other socio-demographic factors. The identified risk factors can be considered when formulating a policy to prevent COVID-19-related suicides, which can impose a long-term economic and health burden on society.

Wearables for Health Tourism: Perspectives and Model Suggestion

Gamze Kose, Liliana Marmolejo-Saucedo, Miriam Rodriguez-Aguilar, Utku Kose — Thu, 18 Jan 2024 00:00:00 +0000

INTRODUCTION: Internet of Things (IoT) has been taking wide place in our daily lives. Among different solution ways in terms of IoT, wearables take a remarkable role because of their compact structures and the mobility. By using wearables, it is very easy to sense a person’s movements and gather characteristic data, which may be processed for desired outcomes if intelligent inferencing. As associated with this, wearables can be effectively used for health tourism operations. As wearables already proved their capabilities for healthcare-oriented applications, the perspective may be directed to health tourism purposes. In this way, positive contributions may be done in the context of not only patients’ well-being but also other actors such as health staff and tourism agencies.

OBJECTIVES: Objective of this paper is to evaluate the potential of wearables in health tourism applications, provide a model suggestion, and evaluate it in the view of different actors enrolling in health tourism ecosystems. Within this objective, research targets were directed to the usage ways of wearables in health tourism, ensuring model structures as meeting with the digital transformation advantages, and gather some findings thanks to feedback by patients, health staff, and agencies.

METHODS: The research firstly included some views on what is health tourism, how the IoT, mobile solutions as well as wearables may be included in the ecosystem. Following to that, the research ensured a model suggestion considering wearables and their connections to health tourism actors. Finally, the potentials of wearables and the model suggestion was evaluated by gathering feedback from potential / active health tourists, health staff, and agency staff.

RESULTS: The research revealed that the recent advancements in wearables and the role of digital transformation affects health tourism. In this context, there is a great potential to track and manage states of all actors in a health tourism eco system. Thanks to data processing and digital systems, it is effective to rise fast and practical software applications for health tourism. In detail, this may be structured in a model where typical IoT and wearable interactions can be connected to sensors, databases, and the related users. According to the surveys done with potential / active health tourists, health staff, and agency staff, such a model has great effect to advance the health tourism.

CONCLUSION: The research study shows positive perspectives for both present and future potentials of wearable and health tourism relation. It is remarkable that rapid advancements in IoT can trigger health tourism and the future of health tourism may be established over advanced applications including data and user-oriented relations.

A Hybrid Optimization Approach for Pulmonary Nodules Segmentation and Classification using Deep CNN

Ajit Narendra Gedam, Aniruddha S. Rumale — Mon, 15 Jan 2024 00:00:00 +0000

Lung Cancer, due to a lower survival rate, is a deadly disease as compared to other cancers. The prior determination of the lung cancer tends to increase the survival rate. Though there are numerous lung cancer detection techniques, they are all insufficient to detect accurate cancer due to variations in the intensity of the CT scan image. For more accuracy in segmentation of CT images, the proposed Elephant-Based Bald Eagle Optimization (EBEO) algorithm is used. This proposed research concentrates on developing a lung nodule detection technique based on Deep learning. To obtain an effective result, the segmentation process will be carried out using the proposed algorithm. Further, the proposed algorithm will be utilized to tune the hyper parameter of the deep learning classifier to increase detection accuracy. It is expected that the proposed state-of-art method will exceed all conventional methods in terms of detection accuracy due to the effectiveness of the proposed algorithm. This survey will be helpful for the healthcare research communities with sufficient knowledge to understand the concepts of the EBEO algorithm and the Deep Convolutional Neural Network for improving the overall human healthcare system.

Analysis of the implementation of teletraining and teleIEC in healthcare services: Case study

Sarita Saavedra, Lloy Pinedo, Tamara Peña — Wed, 06 Mar 2024 00:00:00 +0000

INTRODUCTION: Following the COVID-19 pandemic, telemedicine and telehealth have emerged as crucial technological resources for providing medical care and enhancing the competencies of healthcare professionals.

OBJECTIVES: Analysing the implementation of Teletraining and TeleIEC in the healthcare services of Hospital II-2 Tarapoto in Peru.

METHODS: A basic descriptive study with a mixed cross-sectional approach was conducted. The sample consisted of 266 healthcare specialist professionals and 4293 beneficiaries divided into three groups: healthcare personnel, healthcare students, and community members. The techniques employed included record analysis and surveys, with instruments consisting of a data registration form and a virtual questionnaire.

RESULTS: In 2020, only 18% of professionals participated in teletraining and teleIEC activities. By August 2023, this figure had increased to 38%. It is also evident that the majority of professionals participating in these services as of 2023 were physicians (44%), followed by psychologists (16%), nurses (13%), and nutritionists (11%), reflecting limited participation from dentists (2%), obstetricians (1%), among others.

CONCLUSION: The implementation of teletraining and teleIEC has a positive impact through the strengthening of competencies among professionals, students, and the general public, with learning levels reaching the second and third levels according to Bloom's taxonomy, namely comprehension and application.

Evaluation and Monitoring System for Exercise Rehabilitation Based on Combined Chinese and Western Medicine Technology

Yunxiang Shang — Fri, 01 Mar 2024 00:00:00 +0000

INTRODUCTION: Developing exercise rehabilitation assessment and monitoring systems is essential in rehabilitation medicine. Introduces a sports rehabilitation assessment and monitoring system based on combining Chinese and Western medicine technology, aiming to integrate traditional Chinese and Western medicine theory and modern Western medicine technology to provide more comprehensive and personalized rehabilitation services. Through the systematic integration of technologies, the author is committed to building an efficient and precise rehabilitation system to provide patients with more scientific and practical rehabilitation programs.
OBJECTIVE: The research system employs various sensor technologies, including motion capture devices, biosensors, and pulse recognition technology, by combining Chinese and Western medicine. The motion capture device enables real-time monitoring of the patient's movement trajectory, joint mobility, and other physiological indicators; the biosensor collects the patient's physiological data, such as heart rate and blood pressure. Meanwhile, Chinese and Western medicine pulse recognition technology was introduced to obtain pulse information specific to Chinese and Western medicine to provide more comprehensive data support for rehabilitation assessment. Integrating these technologies, a multi-level and multi-dimensional rehabilitation assessment system was established.
METHODS: This study aims to improve the accuracy and personalization of the rehabilitation assessment and to tailor a rehabilitation plan more in line with the patient's actual situation. Through the combination of Chinese and Western medicine techniques, it aims to break the single perspective of traditional rehabilitation assessment and make the rehabilitation plan closer to the physiological characteristics and pathological state of the patients as well as the needs of the combination of Chinese and Western medicine in the identification and treatment.
RESULTS: The system has achieved remarkable results in practical application. The accurate monitoring of the motion capture device provides a more comprehensive understanding of the patient's motor status and accurately analyzes the rehabilitation progress. At the same time, the data collection of biosensors provides doctors with more detailed physiological information, enabling them to formulate rehabilitation plans more comprehensively. The introduction of combined Chinese and Western medicine pulse recognition technology adds a unique auxiliary diagnostic tool of collaborative Chinese and Western medicine to the rehabilitation assessment and improves the personalized level of the rehabilitation plan.
CONCLUSION: The sports rehabilitation assessment and monitoring system combining Chinese and Western medicine technology brings new ideas and methods for developing rehabilitation medicine. By fully utilizing the advantages of modern technology and traditional medical knowledge, a comprehensive and in-depth rehabilitation assessment system was constructed to provide patients with more scientific and caring rehabilitation services. Future research will optimize the system's performance and promote the broad application of integrated Chinese and Western medicine technology in rehabilitation.

Optimising Deep Neural Networks for Tumour Diagnosis Algorithms Based on Improved MRFO Algorithm

Binbin Han, Fuliang Zhang, Zhenyun Chang, Fang Feng — Mon, 08 Apr 2024 00:00:00 +0000

INTRODUCTION: Cancer has become one of the most prevalent diseases with the highest mortality rate in the world, and timely detection and early acceptance of medical therapeutic interventions are effective means of controlling the progression of cancer patients and improving their post-intervention outcomes.

OBJECTIVES: To make the defects of incomplete features, low accuracy and low real-time performance of current tumour diagnosis methods.

METHODS: This paper proposes a tumour diagnosis method based on the improved MRFO algorithm to improve the optimization process of DBN network parameters. Firstly, the diagnostic features are extracted by analysing the tumour diagnosis identification problem; then, the manta ray foraging optimization algorithm is improved by combining the good point set initialization strategy, the adaptive control parameter strategy and the distribution estimation strategy, and the tumour diagnostic model based on the improved manta ray foraging optimization algorithm to optimize the parameters of the depth confidence network is constructed; finally, the high accuracy and real-time performance of the proposed method are verified by the analysis of simulation experiments.

RESULTS: The results show that the proposed method improves the accuracy of the diagnostic model.

CONLUSION: Addresses the problem of poor accuracy and real-time availability of tumour diagnostic methods.

Real Time Monitoring Research on Rehabilitation Effect of Artificial Intelligence Wearable Equipment on Track and Field Athletes

Bin Wu — Tue, 12 Mar 2024 00:00:00 +0000

INTRODUCTION: With the rapid development of artificial intelligence technology, wearable artificial intelligence devices show great potential in medical rehabilitation. This study explores the Real Time monitoring effect of AI wearable devices in the rehabilitation process of track and field athletes. The application of this technology in rehabilitation monitoring was investigated through the introduction of advanced sensing technology and data analysis algorithms to provide track and field athletes with more scientific and personalized rehabilitation programs.
OBJECTIVES: A group of track and field athletes was selected as the research object and equipped with an artificial intelligence wearable device, which is capable of Real Time monitoring of the athletes' physiological parameters, sports postures, joint mobility, and other rehabilitation-related data. An individualized rehabilitation model was established through the data collected by these sensors, and advanced artificial intelligence algorithms were used to analyze the data in Real Time. At the same time, the sensor data were combined with the actual performance of the athletes' rehabilitation training to comprehensively assess the effectiveness of AI wearable devices in rehabilitation monitoring.
METHODS: This study aims to assess the effect of Real Time monitoring of AI wearable devices in the rehabilitation of track and field athletes and to explore their potential application in the rehabilitation process. Real Time tracking of athletes' physiological status and athletic performance aims to provide more accurate and timely information to rehabilitation doctors and coaches to optimize the rehabilitation training program and promote the rehabilitation process of athletes.
RESULTS: The study showed that artificial intelligence wearable devices have significant Real Time monitoring effects in rehabilitating track and field athletes. Through Real Time monitoring of data such as physiological parameters, sports posture, and joint mobility, the rehabilitation team was able to identify potential problems and adjust the rehabilitation program in a more timely manner. Athletes using artificial intelligence wearable devices improved the personalization and targeting of rehabilitation training, and the rehabilitation effect was significantly better than that of traditional monitoring methods.
CONCLUSION: This study concludes that artificial intelligence wearable devices perform well in rehabilitating track and field athletes, providing a more scientific and comprehensive means of rehabilitation monitoring. Through Real Time tracking, the rehabilitation team could better understand the rehabilitation progress of the athletes, adjust the rehabilitation program in a targeted manner, and improve the rehabilitation effect. However, future research still needs to optimize the performance of the devices further, expand the sample size, and thoroughly study the monitoring needs at different stages of rehabilitation to better meet the individualized requirements of track and field athletes' rehabilitation process.

Basketball Anterior and Posterior Portal Veins Doppler Imaging of Sports Medicine Technique Exploration

Wei Zhu — Fri, 15 Mar 2024 00:00:00 +0000

INTRODUCTION: Basketball, as a high-intensity sport, has attracted much attention for its effects on the cardiovascular system of athletes. The anterior and posterior portal veins are some of the vital blood vessels in the human circulatory system, and their blood flow is closely related to the athletes' physical status. Doppler ultrasound technology is widely used in sports medicine and provides a powerful tool for an in-depth understanding of the effects of basketball on portal vein blood flow. This study aimed to explore the potential of sports medicine technology in assessing cardiovascular adaptations in athletes through portal Doppler imaging before and after basketball exercise.

OBJECTIVES: The primary objective of this study was to analyze the effects of basketball exercise on portal vein blood flow in athletes before and after basketball exercise through the use of Doppler ultrasound technology. Specifically, this study aimed to explore the dynamics of pre- and post-exercise Doppler imaging of the posterior and posterior veins in order to assess the cardiovascular adaptations of athletes during exercise more comprehensively and objectively.

METHODS: A group of healthy professional basketball players were selected as the study subjects, and Doppler ultrasound instruments were utilized to obtain portal Doppler images before, during, and after exercise. The functional status of the vasculature was assessed by analyzing parameters such as portal blood flow velocity and resistance index. At the same time, the physiological parameters of the athletes, such as heart rate and blood pressure, were combined to gain a comprehensive understanding of the effects of basketball on portal blood flow.

RESULTS: The results of the study showed that the anterior and posterior portal blood flow velocities of the athletes changed significantly during basketball exercise. Before the exercise, the blood flow velocity was relatively low, while it rapidly increased and reached the peak state during the exercise. After exercise, blood flow velocity gradually dropped back to the baseline level. In addition, the change in resistance index also indicated that portal blood vessels experienced a particular stress and adaptation process during exercise.

CONCLUSION: This study revealed the effects of exercise on the cardiovascular system of athletes by analyzing the Doppler images of the portal vein before and after basketball exercise. Basketball exercise leads to significant changes in portal hemodynamics, which provides a new perspective for sports medicine. These findings are of guiding significance for the development of training programs for athletes and the prevention of exercise-related cardiovascular problems and provide a valuable reference for further research in the field of sports medicine.

Analysis and Improvement of the Application of Playground Sports Posture Detection Technology in Physical Education Teaching and Training

Jie Xu — Mon, 18 Mar 2024 00:00:00 +0000

INTORDUCTION: The goal of human posture detection technology applied in the field of sports is to realise the indexing of sports norms, to provide scientific guidance for training and teaching, which is of great significance to improve the quality of sports.

OBJECITVES: Aiming at the problems of incomplete features, low accuracy and low real-time performance of sports posture detection and recognition methods.

METHODS: In this paper, a method of sports pose detection based on snow melting heuristic optimisation algorithm of deep limit learning machine network is proposed. Firstly, by analyzing the process of motion pose detection, extracting the feature coordinates of Blaze-Pose and Blaze-Hands key nodes, and constructing the motion pose detection recognition system; then, optimizing the parameters of the deep extreme learning machine network through the snow-melt optimization algorithm, and constructing the motion pose detection recognition model; finally, through simulation experiments and analysis, the accuracy of the proposed method's motion pose detection recognition can reach 95% and the recognition time is less than 0.01 s.

RESULTS: The results show that the proposed method improves the recognition accuracy precision, robustness and real-time performance.

CONCLUSION: The problem of poor generalisation, low accuracy and insufficient real-time performance of the recognition application of the motion pose detection and recognition method is solved.

Wavelet Transform and SVM Based Heart Disease Monitoring for Flexible Wearable Devices

Binbin Han, Fuliang Zhang, Lin Zhao — Tue, 12 Mar 2024 00:00:00 +0000

INTRODUCTION: Heart disease has been a major health challenge globally, therefore the development of reliable and real-time heart disease monitoring methods is crucial for the prevention and management of heart health. The aim of this study is to explore a flexible wearable device approach based on wavelet transform and support vector machine (SVM) to improve the accuracy and portability of heart disease monitoring.
OBJECTIVES: The main objective of this study is to develop a wearable device that combines wavelet transform and SVM techniques to achieve accurate monitoring of physiological signals of heart diseases.
METHODS: An integrated method for heart disease monitoring was constructed using flexible sensor technology combined with a wavelet transform and support vector machine. The Marr wavelet transform was applied to the ECG signals, and the feature vectors were constructed by feature parameter extraction. Then, the radial basis kernel SVM was utilized to identify the three ECG signals. The performance of the algorithm was optimized by adjusting the SVM parameters to improve the accurate monitoring of heart diseases.
RESULTS: The experimental results show that the proposed wavelet transform and SVM-based approach for flexible wearable devices achieves satisfactory results in heart disease monitoring. In particular, the algorithm successfully extracted feature vectors and accurately classified different ECG signals by skillfully combining the wavelet transform and SVM techniques for the processing of premature beat signals.
CONCLUSION: The potential application value of the wavelet transform and SVM-based flexible wearable device approach in heart disease monitoring is emphasized. By efficiently processing ECG signals, the method provides an innovative and comfortable solution for real-time monitoring of cardiac diseases.

Prediction of Diabetic Retinopathy using Deep Learning with Preprocessing

S Balaji, B Karthik, D Gokulakrishnan — Thu, 22 Feb 2024 00:00:00 +0000

INTRODUCTION: When Diabetic Retinopathy (DR) is not identified promptly; it frequently results in sight impairment. To properly diagnose and treat DR, preprocessing of picture methods and precise prediction models are essential. With the help of numerous well-liked filters and a Deep CNN (Convolutional Neural Network) model, the comprehensive method for DR image preparation and prognosis presented in this research is described. Using the filters that focus boundaries and contours in the ocular pictures is the first step in the initial processing stage. This procedure tries to find anomalies linked to DR. By the usage of filters, the excellence of pictures can be developed and minimize disturbances, preserving critical information. The Deep CNN algorithm has been trained to generate forecasts on the cleaned retinal pictures following the phase of preprocessing. The filters efficiently eliminate interference without sacrificing vital data. Convolutional type layers, pooling type layers, and fully associated layers are used in the CNN framework, which was created especially for image categorization tasks, to acquire data and understand the relationships associated with DR.

OBJECTIVES: Using image preprocessing techniques such as the Sobel, Wiener, Gaussian, and non-local mean filters is a promising approach for DR analysis. Then, predicting using a CNN completes the approach. These preprocessing filters enhance the images and prepare them for further examination. The pre-processed images are fed into a CNN model. The model extracts significant information from the images by identifying complex patterns. DR or classification may be predicted by the CNN model through training on a labeled dataset.

METHODS: The Method Preprocessing is employed for enhancing the clarity and difference of retina fundus picture by removing noise and fluctuation. The preprocessing stage is utilized for the normalization of the pictures and non-uniform brightness adjustment in addition to contrast augmentation and noise mitigation to remove noises and improve the rate of precision of the subsequent processing stages.

RESULTS: To improve image quality and reduce noise, preprocessing techniques including Sobel, Wiener, Gaussian, and non-local mean filters are frequently employed in image processing jobs. For a particular task, the non-local mean filter produces superior results; for enhanced performance, it may be advantageous to combine it with a CNN. Before supplying the processed images to the CNN for prediction, the non-local mean filter can assist reduce noise and improve image details.

CONCLUSION: A promising method for DR analysis entails the use of image preprocessing methods such as the Sobel, Wiener, Gaussian, and non-local mean filters, followed by prediction using a CNN. These preprocessing filters improve the photos and get them ready for analysis. After being pre-processed, the photos are sent into a CNN model, which uses its capacity to discover intricate patterns to draw out important elements from the images. The CNN model may predict DR or classification by training it on a labeled dataset. The development of computer-aided diagnosis systems for DR is facilitated by the integration of CNN prediction with image preprocessing filters. This strategy may increase the effectiveness of healthcare workers, boost patient outcomes, and lessen the burden of DR.

Analyzing Machine Learning Classifiers for the Diagnosis of Heart Disease

Saravanan Thangavel, Saravanakumar Selvaraj, Ganesh Karthikeyan V, K Keerthika — Thu, 29 Feb 2024 00:00:00 +0000

INTRODUCTION: Preventable deaths from cardiovascular diseases outnumber all others combined. Detecting it at an early stage is crucial. Human lives will be saved as a result.

OBJECTIVES: Improved cardiac disease prediction using machine learning classifiers is the focus of this article.

METHODS: We have used many different classifiers, such as the support vector machine, naive bayes, random forest, and k-nearest neighbours, to achieve this goal, even though we can’t predict high accuracy in this classifier. So, we have proposed Hyper parameter adjustment was applied to the classifiers, which increased their precision. It was possible to compare the classifiers.

RESULTS: In comparison to other machine learning classifiers, Logistic Regression achieves higher prediction accuracy, at 95.5%.

CONCLUSION: To help people find the nearest cardiac care facilities, Google Maps has been integrated into a responsive web application that has been built for forecasting heart illness.

Impressive predictive model for Breast Cancer based on Machine Learning

Saravanakumar Selvaraj, Saravanan Thangavel, M Prabhakaran, T Sathish — Thu, 29 Feb 2024 00:00:00 +0000

INTRODUCTION: Breast cancer is a major health concern for women all over the world.

OBJECTIVES: In order to reduce mortality rates and provide the most effective treatment, Histopathology image prognosis is essential. When a pathologist examines a biopsy specimen under a microscope, they are engaging in histopathology. The pathologist looks for the picture, determines its type, labels it, and assigns a grade.

METHODS: Tissue architecture, cell distribution, and cellular form all play a role in determining whether a histopathological scan is benign or malignant. Manual picture classification is the slowest and most error-prone method. Automated diagnosis based on machine learning is necessary for early and precise diagnosis, but this challenge has prevented it from being addressed thus far. In this study, we apply curvelet transform to a picture that has been segmented using k-means clustering to isolate individual cell nuclei.

RESULTS: We analysed data from the Wisconsin Diagnosis Breast Cancer database for this article in the context of similar studies in the literature.

CONCLUSION: It is demonstrated that compared to another machine learning algorithm, the IICA-ANN IICA-KNN and IICA-SVM-KNN method using the logistic algorithm achieves 98.04% accuracy.

IMU-Based Approach to Detect Spastic Cerebral Palsy in Infants at Early Stages

N Sukhadia, P Kamboj — Fri, 01 Mar 2024 00:00:00 +0000

INTRODUCTION: Cerebral Palsy (CP) is a non-progressive neurological disorder affecting muscle control in early childhood, leading to permanent alterations in body posture and movement. Early identification is crucial for accurate diagnosis and therapy-based interventions. In recent years, an automated monitoring system has been developed to facilitate the health assessment of infants, enabling early recognition of neurological dysfunctions in high-risk infants. However, the interpretation of these assessments lacks standardization and is subject to examiner bias.

OBJECTIVES: Many infants with CP exhibit increased tonic stretch reflexes due to Upper Motor Neuron Syndrome (UMNS), resulting from motor neuron damage that disrupts muscle signalling.

METHOD: To detect abnormal muscle reactions, our team employed an Inertial Measurement Unit (IMU) sensor, comprising three tri-axial sensors (accelerometer, gyroscope, magnetometer) that capture movement data continuously and unobtrusively. IMU sensors are compact, cost-effective, and have low processing requirements, requiring attachment to the infant's body to measure inter-body part angles. Our team analyzed muscle activity and posture using IMU sensors, collecting tri-axial data from 43 infants in real-time. Additional factors like age, stride length, and leg length were incorporated into the dataset.

RESULTS: Our team has applied various supervised machine learning approaches to predict CP in infants due to the limited dataset size, validating models through k-fold cross-validation. Among the models, Naive Bayes (NB) outperformed Logistic Regression (LR), Decision Tree (DT), Linear Discriminant Analysis (LDA), k-Nearest Neighbors (kNN), and Support Vector Machine (SVM), achieving an accuracy of 88%. CONCLUSION: This research contributes to the early detection and intervention of CP in infants, potentially improving their long-term outcomes.

Analysis on Smart Healthcare Monitoring Based on Compound Dimension

B Vennilapriya, C Bennila Thangammal — Fri, 01 Mar 2024 00:00:00 +0000

INTRODUCTION: Life expectancy has steadily increased in the majority of countries over the last few decades as a result of vast improvements in medical care, public health initiatives, and individual, community hygiene practices as well.

OBJECTIVES: An effective and inexpensive alternative to institutional care was remote health surveillance, which relies on non-invasive and wearable sensors, actuators, and modern statement and information technology to allow the elderly to remain in their familiar homes.

METHODS: With the use of open-source software, widely accessible minimal chipsets, and remote data warehouses for storing, this study details the design and construction of e-health apparel for health monitoring.

RESULTS: By utilizing these devices, medical professionals will be able to track vital signs in real-time, evaluate patients' status, and provide feedback even when they are physically located in a different facility. The next step included creating a wearable system and the garment platform it would be used on.

CONCLUSION: More features were implemented in the form of a smartphone application. This research has potential application in broadening the scope of wearable healthcare systems by investigating the role of apparel in this area.

Comparative Analysis of CNN and Different R-CNN based Model for Prediction of Alzheimer’s Disease

S Roobini, M S Kavitha, S Karthik — Fri, 01 Mar 2024 00:00:00 +0000

INTRODUCTION: Medical images still need to be examined by medical personnel, which is a prolonged and vulnerable progression. The dataset used included 4 classes of 6400 training and test MRI images each and was collected from Kaggle such as cognitively normal (CN), Mild Cognitive Impairment stage (MCI), moderate cognitive impairment (Moderate MCI), and Severe stage of cognitive impairment (AD).

OBJECTIVES: There was a glaring underrepresentation of the Alzheimer Disease (AD) class. The accuracy and effectiveness of diagnoses can be improved with the use of neural network models.

METHODS: In order to establish which CNN-based algorithm performed the multi-class categorization of the AD patient's brain MRI images most accurately. Thus, examine the effectiveness of the popular CNN-based algorithms like Convolutional Neural Network (CNN), Region-based CNN (R-CNN), Fast R-CNN, and Faster R-CNN.

RESULTS: On the confusion matrix, R-CNN performed the best.

CONCLUSION: R-CNN is quick and offers a high precision of 98.67% with a low erroneous measure of 0.0133, as shown in the research.

Detection of Female Anopheles Mosquito-Infected Cells: Exploring CNN, ReLU, and Sigmoid Activation Methods

A L Leena Jenifer, B K Indumathi, C P Mahalakshmi — Fri, 01 Mar 2024 00:00:00 +0000

INTRODUCTION: Deep learning uses multi-layer neural networks where the algorithm decides for itself whether aspects are essential for analysis based on the raw input. In general, deep learning networks get better as more data is used to train them. For a variety of applications, convolutional neural networks are frequently used to analyse, categorize, and detect images.

OBJECTIVES: The proposed system technique is used for automated analysis of malaria-detecting frameworks. A female Anopheles mosquito bite is the primary method of transmission of the blood disease malaria. It is still common to manually count and identify parasitized cells during microscopic examination of either thick or thin layers of haemoglobin, which takes time for disease prognosis.

METHODS: The current research uses a neural network based on convolution to catalogue images of cells with and without malaria infection. This method improves the precision of classification for the datasets under study. The ReLu activation function used by this model enables it to learn more quickly and perform more effectively.

RESULTS: The prediction of infected and healthy cells was done accurately by the proposed model, which uses only 3 layers of convolution, and this was the idea behind the implementation. The model achieved an improved accuracy of 99.77% across 12 iterations (epochs).

CONCLUSION: The proposed model is straightforward and successful in differentiating between malaria-infected and uninfected cells.

An Innovative approach to Improve the Quality of Pharmaceuticals approach using Cloud Computing

Fri, 01 Mar 2024 00:00:00 +0000

INTRODUCTION: Pharmaceuticals evolve alongside advancing technology driven by ongoing research and pharmaceutical companies’ production of new medications. Ongoing research and adjustment are necessary for various aspects of the pharmaceutical sector, such as patient understanding, drug testing, manufacturing, and communication of complex concepts through technology.

OBJECTIVES: This paper discusses the intersection of cloud computing, technological advancements, and healthcare applications.

METHODS: The Azure Cloud facilitates data processing, customer and patient engagement, employee and care team empowerment, clinical and operational optimisation, and healthcare digital transformation in the pharmaceutical industry. The integration of Microsoft Azure cloud technologies inside the pharmaceutical industry is examined in this research. RESULTS: Analysing how Internet of Things (IoT) sensors and the Industrial Internet of Things (IIoT) are used in pharmaceutical manufacturing and logistics, benefits in drug research, production monitoring and supply chain optimisation are highlighted.

CONCLUSION: Cloud computing's potential to facilitate General Data Protection Regulation compliance, improve security, and promote innovation is explored.

A Comprehensive Study on Mental Illness Through Speech and EEG Using Artificial Intelligence

Sanjana Bhat, Reeja S R — Thu, 07 Mar 2024 00:00:00 +0000

A typical mental ailment is depression that considerably harms an individual's everyday activities as well as their mental health. In light of the fact that mental health is one of the biggest problems facing society, researchers have been looking into several strategies for efficiently identifying depression. Mental illness can now be identified through speech analysis thanks to modern artificial intelligence. The speech aids in classifying a patient's mental health status, which could benefit their new study. For the purpose of identifying depression or any other emotion or mood in an individual, a number of past studies based on machine learning and artificial intelligence are being studied. The study also examines the effectiveness of facial expression, photos, emotional chatbots, and texts in identifying a person's emotions. Naive-Bayes, Support Vector Machines (SVM), Linear Support Vectors, Logistic Regression, etc. are ML approaches from text processing. Artificial Neural Network (ANN) is a sort of artificial intelligence method used to extract information from photos and classify them in order to recognise emotions from facial expressions.

Colorectal cancer prediction via histopathology segmentation using DC-GAN and VAE-GAN

R Sujatha, Mahalakshmi K, Mohamed Sirajudeen Yoosuf — Tue, 12 Mar 2024 00:00:00 +0000

Colorectal cancer ranks as the third most common form of cancer in the United States. The Centres of Disease Control and Prevention report that males and individuals assigned male at birth (AMAB) have a slightly higher incidence of colon cancer than females and those assigned female at birth (AFAB) Black humans are more likely than other ethnic groups or races to develop colon cancer. Early detection of suspicious tissues can improve a person's life for 3-4 years. In this project, we use the EBHI-seg dataset. This study explores a technique called Generative Adversarial Networks (GAN) that can be utilized for data augmentation colorectal cancer histopathology Image Segmentation. Specifically, we compare the effectiveness of two GAN models, namely the deep convolutional GAN (DC-GAN) and the Variational autoencoder GAN (VAE-GAN), in generating realistic synthetic images for training a neural network model for cancer prediction. Our findings suggest that DC-GAN outperforms VAE-GAN in generating high-quality synthetic images and improving the neural network model. These results highlight the possibility of GAN-based data augmentation to enhance machine learning models’ performance in medical image analysis tasks. The result shows DC-GAN outperformed VAE-GAN.

A Multi-Model Machine Learning Approach for Monitoring Calories Being Burnt During Workouts Using Smart Calorie Tracer

Wed, 13 Mar 2024 00:00:00 +0000

INTRODUCTION: In today's health-conscious world, accurate calorie monitoring during exercise is crucial for achieving fitness goals and maintaining a healthy lifestyle. However, existing methods often lack precision, driving the need for more reliable tracking systems. This paper explores the use of a multi-model machine learning approach to predict calorie burn during workouts by utilizing a comprehensive dataset.

OBJECTIVES: The objective of this paper is to develop a user-friendly program capable of accurately predicting calorie expenditure during exercise, leveraging advanced machine learning techniques.

METHODS: Techniques from social network analysis were employed to analyze the dataset, which included information on age, gender, height, weight, workout intensity, and duration. Data preprocessing involved handling missing values, eliminating irrelevant columns, and preparing features for analysis. The dataset was then divided into training and testing sets for model development and evaluation. Machine learning models, including Neural Networks, AdaBoost, Random Forest, and Gradient Boosting, were chosen based on their performance in regression tasks.

RESULTS: The neural network model demonstrated superior performance in predicting calorie burn, outperforming other models in terms of MSE, RMSE, and an R2 score. Data visualization techniques aided in understanding the relationship between variables and calorie burn, highlighting the effectiveness of the neural network model.

CONCLUSION: The findings suggest that a multi-model machine learning approach offers a promising solution for accurate calorie tracking during exercise. The neural network model, in particular, shows potential for developing user-friendly calorie monitoring applications. While limitations exist, such as dataset scope and environmental factors, this study lays the groundwork for future advancements in calorie monitoring and contributes to the development of holistic fitness applications.

Clinical Support System for Cardiovascular Disease Forecasting Using ECG

Mon, 18 Mar 2024 00:00:00 +0000

INTRODUCTION: Heart failure is a chronic condition that affects many people worldwide. Regrettably, it is now the biggest cause of mortality globally, and it is becoming more common. Before a cardiac event, early diagnosis of heart disease is challenging. Although healthcare institutions like hospitals and clinics have access to a wealth of heart disease data, it is rarely used to uncover underlying trends.

OBJECTIVES: Algorithms for machine learning (ML) can turn this medical data into insightful information. These methods are used to create decision support systems (DSS) that can gain knowledge from the past and advance. It is essential to use an effective ML-based technique to identify early heart failure and take preventive action to address this worldwide issue. Accurately identifying heart illness is our main goal in this study.

METHODS: For this work, we benchmark different datasets on heart illness, and we use feature engineering approaches to pick the most pertinent qualities for improved performance. Additionally, we assess nine ML methods using critical parameters including precision, f-measure, sensitivity, specificity, and accuracy.

RESULTS: Iterative tests are carried out to evaluate the efficacy of different algorithms. With a flawless cross-validation accuracy score of 99.51% and 100% in all other metrics, our suggested Decision Tree approach performs better than other ML models and cutting-edge studies.

CONCLUSION: Each methodology used in our study is validated using cross-validation techniques. The medical community benefits greatly from this research study.

Interaction between neuroscience and happiness: assessment from Artificial Intelligence advances

Rolando Eslava-Zapata, Verenice Sánchez-Castillo, Edixon Chacón-Guerrero — Mon, 18 Mar 2024 00:00:00 +0000

INTRODUCTION: In recent years, there has been a convergence between Artificial Intelligence and neuroscience, particularly in studying the brain and developing treatments for neurological disorders. Artificial neural networks and deep learning provide valuable insights into neural processing and brain functioning. Recent research tries to explain how neural processes influence an individual's happiness.

OBJECTIVES: To evaluate the interaction between neuroscience and happiness based on the advances in Artificial Intelligence.

METHODS: A bibliometric analysis was performed with articles from the Scopus database in 2013-2023; likewise, the VOSviewer was used for information processing.

RESULTS A total of 603 articles were obtained, and it is evident that the most significant scientific production is centered in the United States (184), United Kingdom (74), and China (73). Three clusters are generated from the Co-occurrence - Author Keywords analysis. The first cluster, red, is related to Artificial Intelligence applications for predicting happiness; the second cluster, green, is associated with Artificial Intelligence tools in neuroscience; and the third cluster, blue, is related to neuroscience in psychology.

CONCLUSION: Neuroscience research has made significant leaps in understanding mental processes such as emotions and consciousness. Neuroscience has encountered happiness and is opening up to an approach that seeks evidence to understand people's well-being supported by Artificial Intelligence.

Gait Data-Driven Analysis of Parkinson’s Disease Using Machine Learning

Archana Panda, Prachet Bhuyan — Tue, 19 Mar 2024 00:00:00 +0000

INTRODUCTION: Parkinson's disease is a progressive and complex neurological condition that mostly affects coordination and motor control. Parkinson's disease is most commonly associated with its motor symptoms, which include tremors, bradykinesia (slowness of movement), rigidity, and postural instability.

OBJECTIVES: Determine any minor alterations in walking patterns that could be early signs of Parkinson's disease. Track the course of Parkinson's disease over time by using gait data.

METHODS: In this study, we applied three types of VGRF datasets ("Dual Tasking, RAS, and Treadmill Walking") and developed an ML-based model using six different classifier methods. The datasets were analysed using 16 sensors, of which 8 were applied to each foot and the total pressure of the left and right foot. The aforementioned three distinct gait patterns movement disorders were the sources of the dataset. The gait signals dataset benefited by the participant demographic data.

RESULTS: Then, we passed the outcome of applying the model and measuring performance through a cross-validation operator to check the accuracy and decision-making of the five algorithms i) Deep Learning, ii) Neural Networks, iii) Support Vector Machine (SVM), iv) Gradient Boost Tree (GBT), v) Random Forest”. The following findings compare the effectiveness of the various algorithms utilized and the observed PD very well.

CONCLUSION: The different ML classifier algorithms demonstrated good detection capability with different accuracy. Our proposed ensemble model is superior to compare with the existing models. Because we can observe the proposed ensemble model result and accuracy better than the other classifier model. The other classifier model’s highest accuracy is 92.08% whereas our ensemble model got 92.31%. So, it has proved that our proposed ensemble model is excellent and robust.

A Step Towards Automated Haematology: DL Models for Blood Cell Detection and Classification

Wed, 20 Mar 2024 00:00:00 +0000

INTRODUCTION: Deep Learning has significantly impacted various domains, including medical imaging and diagnostics, by enabling accurate classification tasks. This research focuses on leveraging deep learning models to automate the classification of different blood cell types, thus advancing hematology practices.

OBJECTIVES: The primary objective of this study is to evaluate the performance of five deep learning models - ResNet50, AlexNet, MobileNetV2, VGG16, and VGG19 - in accurately discerning and classifying distinct blood cell categories: Eosinophils, Lymphocytes, Monocytes, and Neutrophils. The study aims to identify the most effective model for automating hematology processes.

METHODS: A comprehensive dataset containing approximately 8,500 augmented images of the four blood cell types is utilized for training and evaluation. The deep learning models undergo extensive training using this dataset. Performance assessment is conducted using various metrics including accuracy, precision, recall, and F1-score.

RESULTS: The VGG19 model emerges as the top performer, achieving an impressive accuracy of 99% with near-perfect precision and recall across all cell types. This indicates its robustness and effectiveness in automated blood cell classification tasks. Other models, while demonstrating competence, do not match the performance levels attained by VGG19.

CONCLUSION: This research underscores the potential of deep learning in automating and enhancing the accuracy of blood cell classification, thereby addressing the labor-intensive and error-prone nature of traditional methods in hematology. The superiority of the VGG19 model highlights its suitability for practical implementation in real-world scenarios. However, further investigation is warranted to comprehend model performance variations and ensure generalization to unseen data. Overall, this study serves as a crucial step towards broader applications of artificial intelligence in medical diagnostics, particularly in the realm of automated hematology, fostering advancements in healthcare technology.

AI Fuzzy Based Prediction and Prorogation of Alzheimer's Cancer

Srinivas Kolli, Muniyandy Elangovan, M Vamsikrishna, Pramoda Patro — Wed, 20 Mar 2024 00:00:00 +0000

INTRODUCTION: Although decades of experimental and clinical research have shed a lot of light on the pathogenesis of Alzheimer's disease (AD), there are still a lot of questions that need to be answered. The current proliferation of open data-sharing initiatives that collect clinical, routine, and biological data from individuals with Alzheimer's disease presents a potentially boundless wealth of information about a condition.

METHODS: While it is possible to hypothesize that there is no comprehensive collection of puzzle pieces, there is currently a proliferation of such initiatives. This abundance of data surpasses the cognitive capacity of humans to comprehend and interpret fully. In addition, the psychophysiology mechanisms underlying the whole biological continuum of AD may be investigated by combining Big Data collected from multi-omics studies. In this regard, Artificial Intelligence (AI) offers a robust toolbox for evaluating large, complex data sets, which might be used to gain a deeper understanding of AD. This review looks at the recent findings in the field of AD research and the possible obstacles that AI may face in the future.

RESULTS: This research explores the use of CAD tools for diagnosing AD and the potential use of AI in healthcare settings. In particular, investigate the feasibility of using AI to stratify patients according to their risk of developing AD and to forecast which of these patients would benefit most from receiving personalized therapies.

CONCLUSION: To improve these, fuzzy membership functions and rule bases, fuzzy models are trained using fuzzy logic and machine learning.

A Wearable Device for Assistance of Alzheimer’s disease with Computer Aided Diagnosis

Wed, 20 Mar 2024 00:00:00 +0000

INTRODUCTION: Alzheimer’s disease (AD), which is also a pervasive form of dementia primarily common among the elderly, causes progressive brain damage, which might lead to memory loss, language impairment, with cognitive decline. This research proposed a solution that leveraged wearable technology's potential for computer-aided diagnosis. This wearable device, which looks like a pendant, integrates a panic button to notify the closed ones during an emergency.

OBJECTIVES: The primary objective is to effectively scrutinise and implement the wearable device for computer-aided diagnosis in AD. Specifically, this device aims to provide timely alerts to family members during emergencies and other symptoms.

METHODS: The proposed system is developed with the help of a microcontroller and integrates the Android Studio. This device, which resembles a pendant, contains a panic button that connects to a mobile application which receives notifications.

RESULTS: The system successfully achieved its objectives by providing timely alerts with accurate cognitive support for AD patients. The wearable device developed along with the mobile application, with the help of a microcontroller and Android Studio, contributed to the overall well-being of patients with AD.

CONCLUSION: This research introduced a very innovative and promising solution for improving the lives of individuals with AD through this wearable device and mobile application. By addressing these challenges, the system demonstrated its true potential for enhancing the quality of life for individuals with dementia.

Brain Tumor Detection based on Multiple Deep Learning Models for MRI Images

Gokapay Dilip Kumar, Sachi Nandan Mohanty — Thu, 21 Mar 2024 00:00:00 +0000

INTRODUCTION: Medical imaging techniques are used to analyze the inner workings of the human body. In today's scientific world, medical image analysis is the most demanding and rising discipline, with brain tumor being the most deadly and destructive kind of malignancy. A brain tumor is an abnormal growth of cells within the skull that disrupts normal brain function by damaging neighboring cells. Brain tumors are regarded as one of the most dangerous, visible, and potentially fatal illnesses in the world. Because of the fast proliferation of tumor cells, brain tumors kill thousands of people each year all over the world. To save the lives of thousands of individuals worldwide, prompt analysis and automated identification of brain tumors are essential.

OBJECTIVES: To design a enhanced deep learning model for brain tumor detection and classification from MRI analysis.

METHODS: The proposed models Densenet-121, Resnet-101 Mobilenet-V2 is used to perform the task of Brain tumor detection for multi- class classification.

RESULTS: The proposed models achieved an accuracy of up to 99% in our evaluations, and when compared to competing models, they yield superior results.

CONCLUSION: The MRI image collection has been used to train deep learning models. The experimental findings show that the Densnet-121 model delivers the highest accuracy (99%) compared to other models. The system will have significant applications in the medical field. The presence or absence of a tumour can be ascertained using the proposed method.

Predictive Modelling for Heart Disease Diagnosis: A Comparative Study of Classifiers

Fri, 22 Mar 2024 00:00:00 +0000

INTRODUCTION: Cardiovascular diseases, including heart disease, remain a significant cause of morbidity and mortality worldwide. Timely and accurate diagnosis of heart disease is crucial for effective intervention and patient care. With the emergence of machine learning techniques, there is a growing interest in leveraging these methods to enhance diagnostic accuracy and predict disease outcomes.

OBJECTIVES: This study evaluates the performance of three machine learning classifiers—Naive Bayes, Logistic Regression, and k-Nearest Neighbors in predicting heart disease based on patient attributes.

METHODS: In this study, we explore the application of three prominent machine learning classifiers—Naive Bayes, Logistic Regression, and k-Nearest Neighbors (kNN)—to predict the presence of heart disease based on a set of patient attributes.

RESULTS: Using a dataset of 303 patient records with 14 attributes, including age, sex, and cholesterol levels, the data is pre-processed, scaled, and split into training and test sets. Each classifier is trained on the training set and evaluated on the test set. Results reveal that Naive Bayes and k-Nearest Neighbors classifiers outperform Logistic Regression in terms of accuracy, precision, recall, and area under the ROC curve (AUC).

CONCLUSION: This study underscores the promising role of machine learning in medical diagnosis, showcasing the potential of Naive Bayes and k-Nearest Neighbors classifiers in improving heart disease prediction accuracy. Future work could explore advanced classifiers and feature selection techniques to enhance predictive accuracy and generalize findings to larger datasets.

Predictive Modelling for Parkinson's Disease Diagnosis using Biomedical Voice Measurements

Fri, 22 Mar 2024 00:00:00 +0000

INTRODUCTION: Parkinson's Disease (PD), a progressively debilitating neurological disorder impacting a substantial global population, stands as a significant challenge in modern healthcare. The gradual onset of motor and non-motor symptoms underscores the criticality of early detection for optimal treatment outcomes. In response to this urgency, novel avenues for early diagnosis are being explored, where the amalgamation of biomedical voice analysis and advanced machine learning techniques holds immense promise. Individuals afflicted by PD experience a nuanced deterioration of bodily functions, necessitating interventions that are most effective when initiated at an early stage. The potential of biomedical voice measurements to encode subtle health indicators presents an enticing opportunity. The human voice, an intricate interplay of frequencies and patterns, might offer insights into the underlying health condition.

OBJECTIVES: This research embarks on a comprehensive journey to delve into the intricate connections between voice attributes and the presence of PD, with the aim of expediting its detection and treatment.

METHODS: At the heart of this exploration is the Support Vector Machine (SVM) model, a versatile machine learning tool [1-2]. Functioning as a virtual detective, the SVM model learns from historical data to decipher the intricate patterns that differentiate healthy individuals from those with PD [3-4].

RESULTS: Through the power of pattern recognition, the SVM becomes a predictive instrument, a potential catalyst in unravelling the latent manifestations of PD using the unique patterns harbored within the human voice. Embedded within this research are the practical demonstrations showcased through code snippets [5-7]. By synergizing the intricate voice measurements with the SVM model, we envision the emergence of a diagnostic paradigm where early PD detection becomes both accessible and efficient. This study not only epitomizes the synergy of voice and machine interactions but also attests to the transformative potential of technology within the domain of healthcare. .

CONCLUSION: Ultimately, this research strives to harness the intricate layers of voice data, as exemplified through the provided model code [8-11], to contribute to the evolution of an advanced tool for PD prediction. By amalgamating the principles of machine learning and biomedical analysis, we aspire to expedite early PD diagnosis, thereby catalyzing more efficacious treatment strategies. In traversing this multidimensional exploration, we aspire to pave the path toward a future where technology plays an instrumental role in enhancing healthcare outcomes for individuals navigating the challenges of PD, ultimately advancing the pursuit of early diagnosis and intervention.

Heart Disease Prediction Using GridSearchCV and Random Forest

Shagufta Rasheed, G Kiran Kumar, D Malathi Rani, M V V Prasad Kantipudi, Anila M — Fri, 22 Mar 2024 00:00:00 +0000

INTRODUCTION: This study explores machine learning algorithms (SVM, Adaboost, Logistic Regression, Naive Bayes, and Random Forest) for heart disease prediction, utilizing comprehensive cardiovascular and clinical data. Our research enables early detection, aiding timely interventions and preventive measures. Hyperparameter tuning via GridSearchCV enhances model accuracy, reducing heart disease's burdens. Methodology includes preprocessing, feature engineering, model training, and cross-validation. Results favor Random Forest for heart disease prediction, promising clinical applications. This work advances predictive healthcare analytics, highlighting machine learning's pivotal role. Our findings have implications for healthcare and policy, advocating efficient predictive models for early heart disease management. Advanced analytics can save lives, cut costs, and elevate care quality.

OBJECTIVES: Evaluate the models to enable early detection, timely interventions, and preventive measures.

METHODS: Utilize GridSearchCV for hyperparameter tuning to enhance model accuracy. Employ preprocessing, feature engineering, model training, and cross-validation methodologies. Evaluate the performance of SVM, Adaboost, Logistic Regression, Naive Bayes, and Random Forest algorithms.

RESULTS: The study reveals Random Forest as the favored algorithm for heart disease prediction, showing promise for clinical applications. Advanced analytics and hyperparameter tuning contribute to improved model accuracy, reducing the burden of heart disease.

CONCLUSION: The research underscores machine learning's pivotal role in predictive healthcare analytics, advocating efficient models for early heart disease management.

Colorizing Multi-Modal Medical Data: An Autoencoder-based Approach for Enhanced Anatomical Information in X-ray Images

Mon, 25 Mar 2024 00:00:00 +0000

Colourisation is the process of synthesising colours in black and white images without altering the image’s structural content and semantics. The authors explore the concept of colourisation, aiming to colourise the multi-modal medical data through X-rays. Colourized X-ray images have a better potential to portray anatomical information than their conventional monochromatic counterparts. These images contain precious anatomical information that, when colourised, will become very valuable and potentially display more information for clinical diagnosis. This will help improve understanding of these X-rays and significantly contribute to the arena of medical image analysis. The authors have implemented three models, a basic auto-encoder architecture, and two combined learnings of the autoencoder module with transfer learning of pre-trained neural networks. The unique feature of this proposed framework is that it can colourise any medical modality in the medical imaging domain. The framework’s performance is evaluated on a chest x-ray image dataset, and it has produced benchmark results enabling high-quality colourisation. The biggest challenge is the need for a correct solution for the mapping between intensity and colour. This makes human interaction and external information from medical professionals crucial for interpreting the results.

Integrated Intelligent Computing Models for Cognitive-Based Neurological Disease Interpretation in Children: A Survey

Archana Tandon, Bireshwar Dass Mazumdar, Manoj Kumar Pal — Mon, 25 Mar 2024 00:00:00 +0000

INTRODUCTION: This piece of work provides the description of integrated intelligent computing models for the interpretation of cognitive-based neurological diseases in children. These diseases can have a significant impact on children's cognitive and developmental functioning.

OBJECTIVES: The research work review the current diagnosis and treatment methods for cognitive based neurological diseases and discusses the potential of machine learning, deep learning, Natural language processing, speech recognition, brain imaging, and signal processing techniques in interpreting the diseases.

METHODS: A survey of recent research on integrated intelligent computing models for cognitive-based neurological disease interpretation in children is presented, highlighting the benefits and limitations of these models.

RESULTS: The significant of this work provide important implications for healthcare practice and policy, with strengthen diagnosis and treatment of cognitive-based neurological diseases in children.

CONCLUSION: This research paper concludes with a discussion of the ethical and legal considerations surrounding the use of intelligent computing models in healthcare, as well as future research directions in this area.

Extreme Learning Machine for Biomedical Image Classification: A Multi-Case Study

Mon, 25 Mar 2024 00:00:00 +0000

In the current realm of biomedical image classification, the predominant choice remains deep learning networks, particularly convolutional neural network (CNN) models. However, deep learning suffers from a notable drawback in terms of its high training cost, mainly due to intricate data models. A recent alternative, known as the Extreme Learning Machine (ELM), has emerged as a promising solution. Empirical investigations have indicated that ELM can offer satisfactory predictive performance for a wide array of classification tasks, while significantly reducing training costs when compared to deep learning networks trained using back propagation.
This research paper introduces a methodology designed to evaluate the suitability of employing the Extreme Learning Machine for biomedical classification tasks. Our study encompasses binary and multiclass classification across four distinct scenarios, involving the analysis of biomedical images obtained from both dermatoscopes and blood cell microscopes. The findings underscore the effectiveness of the Extreme Learning Machine, showcasing its successful utilization in the classification of biomedical images.

From Pixels to Pathology: The Power of CNNs in Detecting Tuberculosis

Mon, 25 Mar 2024 00:00:00 +0000

INTRODUCTION: Tuberculosis (TB) remains a significant global health threat, demanding trustworthy and effective detection techniques. This study investigates the utilization of deep learning models, specifically ResNet50, InceptionV3, AlexNet, DenseNet121, and Inception3, for diagnosing tuberculosis from chest X-ray images. With a substantial dataset comprising 4,000 chest X-ray images, sourced from seven different nations and categorized as TB-infected or normal, this research aims to evaluate the performance of various deep learning architectures in accurately distinguishing TB instances.
OBJECTIVES: The primary objective of this study is to assess the efficacy of different deep learning models in differentiating TB instances from chest X-ray images. By employing segmentation, data augmentation, and image pre-processing techniques, the research aims to enhance model performance and reliability in TB diagnosis.
METHODS: The chest X-ray image dataset, scaled to 224x224 pixels, underwent segmentation, data augmentation, and pre-processing before being fed into the deep learning models. The dataset was divided into 80% for model training and 20% for testing, utilizing a five-fold cross-validation technique. Performance evaluation metrics including accuracy, precision, recall, and F1-score were employed to assess the models' effectiveness in TB identification.
RESULTS: The findings indicate that ResNet50 and InceptionV3 models achieved near-perfect accuracy, precision, recall, and F1-scores, demonstrating their potential as reliable methods for TB identification. Despite exhibiting lower accuracy for the TB class, AlexNet also displayed good performance. However, DenseNet121 and Inception3 models showed room for improvement, particularly in precision and recall for the TB class.
CONCLUSION: This study underscores the potential of deep learning models in enhancing TB identification in chest X-ray images. It highlights the importance of segmentation, data augmentation, and image pre-processing techniques in improving model performance. Future research may explore hyperparameter tuning, alternative data augmentation strategies, and ensemble approaches to optimize the performance of these models further. Overall, this work contributes to the growing body of knowledge on the application of artificial intelligence in healthcare, particularly in disease diagnosis and detection.

Enhancing Health Product Traceability on the Blockchain: A Novel Approach for Supply Chain Management inspection to AI

Mon, 25 Mar 2024 00:00:00 +0000

INTRODUCTION: Blockchain technology is being investigated as a viable solution due to the industry's growing requirement for accountability and traceability. This study describes a fresh method for tracking down medical products that makes use of a decentralised smart contract network set up on the Ethereum blockchain. In order to enable secure and auditable tracking of health products throughout their lifecycle, the suggested system, named "HealthProductTraceability," makes use of the transparency and immutability of blockchain.

OBJECTIVES: The system uses a "Product" struct to hold pertinent data such the product name, batch number, temperature, producer, and distributors. To quickly get product information depending on the batch number, a mapping is used. The use of tools to manufacture items, send them to distributors, and market them is one significant contribution of this research.By demanding validation tests, such as verifying that batch numbers are unique and exist before carrying out certain activities, these functions protect the integrity of the traceability system.

METHODS: In order to enable interested parties to track the product's travel and temperature changes, the system additionally emits events for product manufacture, distribution, and temperature adjustments. The suggested system is innovative because it can track the temperature of health items from beginning to end on a decentralised, open platform.

RESULTS: By utilising blockchain technology, the system lessens reliance on centralised authorities, fosters stakeholder trust, and minimises the likelihood of fraud, forgery, and tampering in the supply chain for health products. The contract's architecture recognises some of the issues with blockchain technology, including scalability and privacy. By investigating solutions like sidechains, off-chain transactions, and enhancements to consensus methods, scalability issues are solved.

CONCLUSION: In summary, the suggested HealthProductTraceability system offers a creative and practical solution to the traceability issues facing the health product sector. The solution provides improved transparency, security, and accountability by utilising blockchain technology, paving the path for a more dependable and trustworthy health product supply chain. To increase the system's usefulness and adoption in real-world circumstances, further research can investigate scalability and privacy issues.

Deep Learning in Medical Imaging: A Case Study on Lung Tissue Classification

Tue, 26 Mar 2024 00:00:00 +0000

INTRODUCTION: In the field of medical imaging, accurate categorization of lung tissue is essential for timely diagnosis and management of lung-related conditions, including cancer. Deep Learning (DL) methodologies have revolutionized this domain, promising improved precision and effectiveness in diagnosing ailments based on image analysis. This research delves into the application of DL models for classifying lung tissue, particularly focusing on histopathological imagery.

OBJECTIVES: The primary objective of this study is to explore the deployment of DL models for the classification of lung tissue, emphasizing histopathological images. The research aims to assess the performance of various DL models in accurately distinguishing between different classes of lung tissue, including benign tissue, lung adenocarcinoma, and lung squamous cell carcinoma.

METHODS: A dataset comprising 9,000 histopathological images of lung tissue was utilized, sourced from HIPAA compliant and validated sources. The dataset underwent augmentation to ensure diversity and robustness. The images were categorized into three distinct classes and balanced before being split into training, validation, and testing sets. Six DL models - DenseNet201, EfficientNetB7, EfficientNetB5, Vgg19, Vgg16, and Alexnet - were trained and evaluated on this dataset. Performance assessment was conducted based on precision, recall, F1-score for each class, and overall accuracy.

RESULTS: The results revealed varying performance levels among the DL models, with EfficientNetB5 achieving perfect scores across all metrics. This highlights the capability of DL in improving the accuracy of lung tissue classification, which holds promise for enhancing diagnosis and treatment outcomes in lung-related conditions.

CONCLUSION: This research significantly contributes to understanding the effective utilization of DL models in medical imaging, particularly for lung tissue classification. It emphasizes the critical role of a diverse and balanced dataset in developing robust and accurate models. The insights gained from this study lay the groundwork for further exploration into refining DL methodologies for medical imaging applications, with a focus on improving diagnostic accuracy and ultimately, patient outcomes.

Exploring Deep Learning Models for Accurate Alzheimer's Disease Classification based on MRI Imaging

Tue, 26 Mar 2024 00:00:00 +0000

INTRODUCTION: Alzheimer's disease (AD), a complex neurodegenerative condition, presents significant challenges in early and accurate diagnosis. Early prediction of AD severity holds the potential for improved patient care and timely interventions. This research investigates the use of deep learning methodologies to forecast AD severity utilizing data extracted from Magnetic Resonance Imaging (MRI) scans.

OBJECTIVES: This study aims to explore the efficacy of deep learning models in predicting the severity of Alzheimer's disease using MRI data. Traditional diagnostic methods for AD, primarily reliant on cognitive assessments, often lead to late-stage detection. MRI scans offer a non-invasive means to examine brain structure and detect pathological changes associated with AD. However, manual interpretation of these scans is labor-intensive and subject to variability.

METHODS: Various deep learning models, including Convolutional Neural Networks (CNNs) and advanced architectures like DenseNet, VGG16, ResNet50, MobileNet, AlexNet, and Xception, are explored for MRI scan analysis. The performance of these models in predicting AD severity is assessed and compared. Deep learning models autonomously learn hierarchical features from the data, potentially recognizing intricate patterns associated with different AD stages that may be overlooked in manual analysis.

RESULTS: The study evaluates the performance of different deep learning models in predicting AD severity using MRI scans. The results highlight the efficacy of these models in capturing subtle patterns indicative of AD progression. Moreover, the comparison underscores the strengths and limitations of each model, aiding in the selection of appropriate methodologies for AD prognosis.

CONCLUSION: This research contributes to the growing field of AI-driven healthcare by showcasing the potential of deep learning in revolutionizing AD diagnosis and prognosis. The findings emphasize the importance of leveraging advanced technologies, such as deep learning, to enhance the accuracy and timeliness of AD diagnosis. However, challenges remain, including the need for large, annotated datasets, model interpretability, and integration into clinical workflows. Continued efforts in this area hold promise for improving the management of AD and ultimately enhancing patient outcomes.

Convolutional Neural Networks in Malaria Diagnosis: A Study on Cell Image Classification

Tue, 26 Mar 2024 00:00:00 +0000

INTRODUCTION: Malaria, a persistent global health threat caused by Plasmodium parasites, necessitates rapid and accurate identification for effective treatment and containment. This study investigates the utilization of convolutional neural networks (CNNs) to enhance the precision and speed of malaria detection through the classification of cell images infected with malaria.

OBJECTIVES: The primary objective of this research is to explore the effectiveness of CNNs in accurately classifying malaria-infected cell images. By employing various deep learning models, including ResNet50, AlexNet, Inception V3, VGG19, VGG16, and MobileNetV2, the study aims to assess the performance of each model and identify their strengths and weaknesses in malaria diagnosis.

METHODS: A balanced dataset comprising approximately 8,000 enhanced images of blood cells, evenly distributed between infected and uninfected classes, was utilized for model training and evaluation. Performance evaluation metrics such as precision, recall, F1-score, and accuracy were employed to assess the efficacy of each CNN model in malaria classification.

RESULTS: The results demonstrate high accuracy across all models, with AlexNet and VGG19 exhibiting the highest levels of accuracy. However, the selection of a model should consider specific application requirements and constraints, as each model presents unique trade-offs between computational efficiency and performance.

CONCLUSION: This study contributes to the burgeoning field of deep learning in healthcare, particularly in utilizing medical imaging for disease diagnosis. The findings underscore the considerable potential of CNNs in enhancing malaria diagnosis. Future research directions may involve further model optimization, exploration of larger and more diverse datasets, and the integration of CNNs into practical diagnostic tools for real-world deployment.

Exploring the Potential of Deep Learning in the Classification and Early Detection of Parkinson's Disease

Wed, 27 Mar 2024 00:00:00 +0000

INTRODUCTION: Parkinson's Disease (PD) is a progressive neurological disorder affecting a significant portion of the global population, leading to profound impacts on daily life and imposing substantial burdens on healthcare systems. Early identification and precise classification are crucial for effectively managing this disease. This research investigates the potential of deep learning techniques in facilitating early recognition and accurate classification of PD.

OBJECTIVES: The primary objective of this study is to leverage advanced deep learning techniques for the early detection and precise classification of Parkinson's Disease. By utilizing a rich dataset comprising speech signal features extracted from 3000 PD patients, including Time Frequency Features, Mel Frequency Cepstral Coefficients (MFCCs), Wavelet Transform based Features, Vocal Fold Features, and TWQT features, this research aims to evaluate the performance of various deep learning models in PD classification.

METHODS: The dataset containing diverse speech signal features from PD patients' recordings serves as the foundation for training and evaluating five different deep learning models: ResNet50, VGG16, Inception v2, AlexNet, and VGG19. Each model undergoes training and assessment to determine its capability in accurately classifying PD patients. Performance metrics such as accuracy are employed to evaluate the models' effectiveness.

RESULTS: The results demonstrate promising potential, with overall accuracies ranging from 89% to 95% across the different deep learning models. Notably, AlexNet emerges as the top-performing model, achieving an accuracy of 95% and demonstrating balanced performance in accurately identifying both true and false PD cases.

CONCLUSION: This research highlights the significant potential of deep learning in facilitating the early detection and classification of Parkinson's Disease. Leveraging speech signal features offers a non-invasive and cost-effective approach to PD assessment. The findings contribute to the growing body of evidence supporting the integration of artificial intelligence in healthcare, particularly in the realm of neurodegenerative disorders. Further exploration into the application of deep learning in this domain holds promise for advancing PD diagnosis and management.

Forecasting Epileptic Seizures Using XGBoost Methodology and EEG Signals

Sunkara Mounika, Reeja S R — Wed, 27 Mar 2024 00:00:00 +0000

INTRODUCTION: Epilepsy denotes a disorder of neurological origin marked by repetitive and spontaneous seizures without any apparent trigger. Seizures occur due to abrupt and heightened electricity flowing through the brain, which can lead to physical and mental symptoms. There are several types of epileptic seizures, and epilepsy itself can be caused by various underlying conditions. EEG (Electroencephalogram) is one of the most important and widely used tools for epileptic seizure prediction and diagnosis. EEG uses skull sensors to record electrical signals from the brain., and it can provide valuable insights into brain activity patterns associated with seizures.

OBJECTIVES: Brain-computer interface technology pathway for analyzing the EEG signals for seizure prediction to eliminate the class imbalance issue from our dataset in this case, a SMOTE approach is applied. It is observable that there are more classes of one variable than there are of the others in the output variable. This will be problematic when employing different Artificial intelligence techniques since these algorithms are more likely to be biased towards a certain variable because of its high prevalence

METHODS: SMOTE approaches will be used to address this bias and balance the number of variables in the response variable. To develop an XGBoost (Extreme Gradient Boosting) model using SMOTE techniques to increase classification accuracy.

RESULTS: The results show that the XGBoost method achieves a 98.7% accuracy rate.

CONCLUSION: EEG-based model for seizure type using the XGBoost model for predicting the disease early. The Suggested method could significantly reduce the amount of time needed to accomplish seizure prediction.

Rule Based Mamdani Fuzzy Inference System to Analyze Efficacy of COVID19 Vaccines

Poonam Mittal, S P Abirami, Puppala Ramya, Balajee J, Elangovan Muniyandy — Wed, 27 Mar 2024 00:00:00 +0000

INTRODUCTION: COVID-19 was declared as most dangerous disease and even after maintaining so many preventive measures, vaccination is the only preventive option from SARS-CoV-2. Vaccination has controlled the risk and spreading of virus that causes COVID-19. Vaccines can help in preventing serious illness and death. Before recommendation of COVID-19 vaccines, clinical experiments are being conducted with thousands of grown person and children. In controlled situations like clinical trials, efficacy refers to how well a vaccination prevents symptomatic or asymptomatic illness.

OBJECTIVES: The effectiveness of a vaccine relates to how effectively it works in the actual world.

METHODS: This research presents a novel approach to model the efficacy of COVID’19 vaccines based on Mamdani Fuzzy system Modelling. The proposed fuzzy model aims to gauge the impact of epidemiological and clinical factors on which the efficacy of COVID’19 vaccines.

RESULTS: In this study, 8 different aspects are considered, which are classified as efficiency evaluating factors. To prepare this model, data has been accumulated from various research papers, reliable news articles on vaccine response in multiple regions, published journals etc. A set of Fuzzy rules was inferred based on classified parameters. This fuzzy inference system is expected to be of great help in recommending the most appropriate vaccine on the basis of several parameters.

CONCLUSION: It aims to give an idea to pharmaceutical manufacturers on how they can improve vaccine efficacy and for the decision making that which one to be followed.

X-ray body Part Classification Using Custom CNN

Reeja S R, Sangameswar J, Solomon Joseph Joju, Mrudhul Reddy Gangula, Sujith S — Thu, 28 Mar 2024 00:00:00 +0000

INTRODUCTION: This work represents a significant step forward by harnessing the power of deep learning to classify X-ray images into distinct body parts. Over the years X-ray pictures were evaluated manually.

OBJECTIVE: Our aim is to automate X-ray interpretation using deep learning techniques.

METHOD: Leveraging cutting-edge frameworks such as FastAI and TensorFlow, a Convolutional Neural Network (CNN) has been meticulously trained on a dataset comprising DICOM images and their corresponding labels.

RESULT: The results achieved by the model are indeed promising, as it demonstrates a remarkable ability to accurately identify various body parts. CNN shows 97.38% performance by compared with other classifiers.

CONCLUSION: This innovation holds the potential to revolutionize medical diagnosis and treatment planning through the automation of image analysis, marking a substantial leap forward in the field of healthcare technology.

SAA: A novel skin lesion Shape Asymmetry Classification Analysis

Shaik Reshma, Reeja S R — Thu, 28 Mar 2024 00:00:00 +0000

INTRODUCTION: Skin cancer is emerging as a significant health risk. Melanoma, a perilous kind of skin cancer, prominently manifests asymmetry in its morphological characteristics.

OBJECTIVE: The objective of the study is to classify the asymmetry of the skin lesion shape accurately and to find the number of symmetric lines and the angles of formation of symmetric lines.

METHOD: This study introduces a unique methodology known as Shape Asymmetry Analysis (SAA). The SAA incorporates a comprehensive framework including image pre-processing, segmentation along with the computation of mean deviation error and the subsequent categorization of data into symmetric and asymmetric forms using a classification model.

RESULT: The PH2 dataset is used in this study, where the three labels are consolidated into two categories. Specifically, the labels "symmetric" and "symmetric with one axis" are merged and classified as "symmetric," while the label "asymmetric" is unchanged and classified as "asymmetric". The model demonstrates superior performance compared to conventional methodologies, achieving a noteworthy accuracy rate of 90%. Additionally, it exhibits a weighted F1-score, precision, and recall of 0.89,0.91,0.90 respectively.

CONCLUSION: The SAA model accurately classifies skin lesion shapes compared to state-of-the-art methods. The model can be applied to the shapes, irrespective of irregularity, to find symmetric lines and angles.

Swift Diagnose: A High-Performance Shallow Convolutional Neural Network for Rapid and Reliable SARS-COV-2 Induced Pneumonia Detection

Thu, 28 Mar 2024 00:00:00 +0000

INTRODUCTION: The SARS-COV-2 pandemic has led to a significant increase in the number of infected individuals and a considerable loss of lives. Identifying SARS-COV-2-induced pneumonia cases promptly is crucial for controlling the virus's spread and improving patient care. In this context, chest X-ray imaging has become an essential tool for detecting pneumonia caused by the novel coronavirus.

OBJECTIVES: The primary goal of this research is to differentiate between pneumonia cases induced specifically by the SARS-COV-2 virus and other types of pneumonia or healthy cases. This distinction is vital for the effective treatment and isolation of affected patients.

METHODS: A streamlined stacked Convolutional Neural Network (CNN) architecture was employed for this study. The dataset, meticulously curated from Johns Hopkins University's medical database, comprised 2292 chest X-ray images. This included 542 images of COVID-19-infected cases and 1266 non-COVID cases for the training phase, and 167 COVID-infected images plus 317 non-COVID images for the testing phase. The CNN's performance was assessed against a well-established CNN model to ensure the reliability of the findings.

RESULTS: The proposed CNN model demonstrated exceptional accuracy, with an overall accuracy rate of 98.96%. In particular, the model achieved a per-class accuracy of 99.405% for detecting SARS-COV-2-infected cases and 98.73% for identifying non-COVID cases. These results indicate the model's significant potential in distinguishing between COVID-19-related pneumonia and other conditions.

CONCLUSION: The research validates the efficacy of using a specialized CNN architecture for the rapid and precise identification of SARS-COV-2-induced pneumonia from chest X-ray images. The high accuracy rates suggest that this method could be a valuable tool in the ongoing fight against the COVID-19 pandemic, aiding in the swift diagnosis and effective treatment of patients.

Safeguarding Patient Privacy: Exploring Data Protection in E-Health Laws: A Cross-Country Analysis

Thu, 28 Mar 2024 00:00:00 +0000

INTRODUCTION: Health information amassed during the treatment of a medical condition is termed health data. This data encompasses information gathered about a patient and their family, forming a patient history. The internet has progressively transformed communication, commerce, and information acquisition. Among the diverse domains it has influenced, the healthcare sector stands out as one of the most intricate and unique realms of integration. Big data are the results of normal online transactions and interactions that take place online, the detectors that are implanted in devices and actual locations, as well as the generation of digital contents by individuals whenever they submit data over internet.

OBJECTIVES: The need of protection of health data and methods of safeguarding patient privacy. The study also helps \understand and appreciate the best practices which will help India in implementing the law more effectively.

METHODS: A doctrinal method of research was employed to analyse the laws and regulations. A comparative approach of different countries gives us the understanding of the gaps and issues. The efficacy of the laws was tested as the paper explores the laws of Canada & Indonesia regarding data protection.

RESULTS: In this study, we understood the generation, processing, and interchange of these massive amounts of data can now be facilitated by cloud computing technology. As India, recently enacted ‘The Digital Data Protection Act 2023’ which might be a ray of hope for protection of sensitive health data of individuals from misuse.

CONCLUSION: The journey towards optimal data protection is ongoing, requiring continuous adaptation to the dynamic nature of technology and the ever-changing healthcare environment.

Influenza Diagnosis Deep Learning: Machine Learning Approach for Pharyngeal Image Infection

Tue, 02 Apr 2024 00:00:00 +0000

INTRODUCTION: Annual influenza epidemics and rare pandemics represent a significant global health risk. Since the upper respiratory tract is the primary target of influenza, a diagnosis of influenza illness might be made using deep learning applied to pictures of the pharynx. Using pharyngeal imaging data and clinical information, the researcher created a deep-learning model for influenza diagnosis. People who sought medical attention for flu-like symptoms were the subjects included.

METHODOLOGY: The study created a diagnostic and predicting Artificial Intelligence (AI) method using deep learning techniques to forecast clinical data and pharyngeal pictures for PCR confirmation of influenza. The accuracy of the AI method as a diagnostic tool was measured during the validation process. The extra research evaluated the AI model's diagnosis accuracy to that of three human doctors and explained the methodology using high-impact heat maps. In the training stage, a cohort of 8,000 patients was recruited from 70 hospitals. Subsequently, a subset of 700 patients, including 300 individuals with PCR-confirmed influenza, was selected from 15 hospitals during the validation stage.

RESULTS: The AI model exhibited an operating receiver curve with an area of 1.01, surpassing the performance of three doctors by achieving a sensitivity of 80% and a specificity of 80%. The significance of heat maps lies in their ability to provide valuable insights. In AI models, particular attention is often directed towards analyzing follicles on the posterior pharynx wall. Researchers introduced a novel artificial intelligence model that can assist medical professionals in swiftly diagnosing influenza based on pharyngeal images.

An Effective analysis of brain tumor detection using deep learning

Yenumala Sankararao, Syed Khasim — Wed, 03 Apr 2024 00:00:00 +0000

INTRODUCTION: Cancer remains a significant health concern, with early detection crucial for effective treatment. Brain tumors, in particular, require prompt diagnosis to improve patient outcomes. Computational models, specifically deep learning (DL), have emerged as powerful tools in medical image analysis, including the detection and classification of brain tumors. DL leverages multiple processing layers to represent data, enabling enhanced performance in various healthcare applications.

OBJECTIVES: This paper aims to discuss key topics in DL relevant to the analysis of brain tumors, including segmentation, prediction, classification, and assessment. The primary objective is to employ magnetic resonance imaging (MRI) pictures for the identification and categorization of brain malignancies. By reviewing prior research and findings comprehensively, this study provides valuable insights for academics and professionals in deep learning seeking to contribute to brain tumor identification and classification.

METHODS: The methodology involves a systematic review of existing literature on DL applications in brain tumor analysis, focusing on MRI imaging. Various DL techniques, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and hybrid models, are explored for their efficacy in tasks such as tumor segmentation, prediction of tumor characteristics, classification of tumor types, and assessment of treatment response.

RESULTS: The review reveals significant advancements in DL-based approaches for brain tumor analysis, with promising results in segmentation accuracy, tumor subtype classification, and prediction of patient outcomes. Researchers have developed sophisticated DL architectures tailored to address the complexities of brain tumor imaging data, leading to improved diagnostic capabilities and treatment planning.

CONCLUSION: Deep learning holds immense potential for revolutionizing the diagnosis and management of brain tumors through MRI-based analysis. This study underscores the importance of leveraging DL techniques for accurate and efficient brain tumor identification and classification. By synthesizing prior research and highlighting key findings, this paper provides valuable guidance for researchers and practitioners aiming to contribute to the field of medical image analysis and improve outcomes for patients with brain malignancies.

Distance Analysis and Dimensionality Reduction using PCA on Brain Tumour MRI Scans

Thu, 04 Apr 2024 00:00:00 +0000

INTRODUCTION: Compression of MRI images while maintaining essential information, makes it easier to distinguish between different types of brain tumors. It also assesses the effect of PCA on picture representation modification and distance analysis between tumor classes.
OBJECTIVES: The objective of this work is to enhance the interpretability and classification accuracy of highdimensional MRI scans of patients with brain tumors by utilising Principle Component Analysis (PCA) to reduce their complexity.
METHODS:This study uses PCA to compress high-dimensional MRI scans of patients with brain tumors, focusing on improving classification using dimensionality reduction approaches and making the scans easier to understand.
RESULTS: PCA efficiently reduced MRI data, enabling better discrimination between different types of brain tumors and significant changes in distance matrices, which emphasize structural changes in the data.
CONCLUSION: PCA is crucial for improving the interpretability of MRI data.

Sentiment Analysis of Covid Vaccine Myths using Various Data Visualization Tools

Tarandeep Kaur Bhatia, Samagya Rathi, Thipendra P Singh, Biswayan Naha — Thu, 04 Apr 2024 00:00:00 +0000

INTRODUCTION: Anti-vaccination agitation is on the rise, both in-person and online, notably on social media. The Internet has become the principal source of health-related information and vaccines for an increasing number of individuals. This is worrisome since, on social media, any comment, whether from a medical practitioner or a layperson, has the same weight. As a result, low-quality data may have a growing influence on vaccination decisions for children.

OBJECTIVES: This paper will evaluate the scale and type of vaccine-related disinformation, the main purpose was to discover what caused vaccine fear and anti-vaccination attitudes among social media users.

METHODS: The vaccination-related data used in this paper was gathered from Reddit, an information-sharing social media network with about 430 million members, to examine popular attitudes toward the vaccine. The materials were then pre-processed. External links, punctuation, and bracketed information were the first things to go. All text was also converted to lowercase. This was followed by a check for missing data. This paper is novel and different as Matplotlib, pandas, and word cloud was used to create word clouds and every result has a visual representation. The Sentiment analysis was conducted using the NLTK library as well as polarity and subjectivity graphs were generated.

RESULTS: It was discovered that the majority population had neutral sentiments regarding vaccination. Data visualization methods such as bar charts showed that neutral sentiment outnumbers both positive and negative sentiment.

CONCLUSION: Prevalent Sentiment has a big influence on how people react to the media and what they say, especially as people utilize social media platforms more and more. Slight disinformation and/or indoctrination can quickly turn a neutral opinion into a negative one.

Pre-processing the Photoplethysmography Signals for Enhancing the Cardiovascular Diseases Detection for Wrist Pulse Analysis in Nadi Ayurveda

Aditya Tandon, Vivek Kumar, Tanupriya Choudhury — Thu, 04 Apr 2024 00:00:00 +0000

INTRODUCTION: In recent years, Photoplethysmography (PPG) signal has played a vital role in detecting Cardiovascular Diseases (CVDs) in case of wrist pulse analysis emulating the Nadi Ayurveda. The PPG signals acquired from the sensor measurement are severely distorted by various artifacts, which significantly impact the accuracy of disease detection and hamper the disease diagnosis process.

OBJECTIVES: Removing the noises is essential before detecting CVDs from the signals and thus, developing a simple and effective noise reduction method for enhancing the PPG signal quality constitutes a challenging research problem, particularly with prominent artifacts.

METHODS: This paper designs an effective pre-processing technique that improves denoising methods to enhance the PPG signal quality. The design of pre-processing technique contains two major phases: Primary denoising-based artifact removal and secondary denoising-based Premature Ventricular Contraction (PVC) detection and Power-Line Interference (PLI) noise removal. The primary denoising method involves coarse and fine-grained filtering. The coarse-grained filtering removes the major artifacts, such as Baseline Wander (BLW) and Motion Artifacts (MA), by developing the Two-Stage Adaptive Noise Canceller (TANC) method. The fine-grained filtering process utilizes a detrended filter to filter the refined signal obtained from the TANC method. For the signals filtered from the primary denoising method, the secondary denoising method targets to detect the PVC-induced PPG signals from the decomposed high-frequency signals and removes high-frequency noise, such as PLI from noisy signals, by adopting the Wavelet Transform (WT) method.

RESULTS: During the signal reconstruction process in the WT method, the research work reconstructs the denoised PPG signals along with the PVC-induced PPG signals. The experimental results of the noise removal methodology illustrated significant improvements in PPG signal quality.

CONCLUSION: The designed pre-processing technique effectively denoises PPG signals, leading to enhanced signal quality which can further aid in accurate disease detection.

Unveiling the Biomarkers: Identifying Key Signatures for Cancer Hallmarks

Shikha Verma, Aditi Sharan — Fri, 05 Apr 2024 00:00:00 +0000

INTRODUCTION: Finding biomarkers that are closely associated with cancer-related traits is critical to the advancement of cancer research, especially when it comes to personalised treatment. The objective of this research is to explore multiple biomarker categories, including genetics, proteins, and chemicals, in order to better understand the complex terrain of cancer.

OBJECTIVES: Few of the objectives include examining a variety of biomarker types, such as chemical, protein, and genetic markers and determining which important biomarker signatures correspond to each cancer hallmark.

Also the study aims to perform a comparative analysis to show how the SVM model's features incorporating identified biomarkers improves classification performance.

METHODS: The study includes NLP and ML techniques for the identification and classification of biomarkers for the hallmark of cancer dataset.

RESULTS: The discovery of important biomarker signatures connected to every cancer hallmark is one of the study's primary findings. In addition, our new SVM-based classification model performed well in the multilabel text classification of PubMed abstracts, showing a significant improvement in performance when the biomarkers were used as features.

CONCLUSION: To sum up, this study makes a substantial contribution to the area of cancer research by identifying important biomarker signatures connected to many cancer hallmarks.

Glaucoma Detection Using Explainable AI and Deep Learning

Najeeba Afreen, Rajanikanth Aluvalu — Fri, 05 Apr 2024 00:00:00 +0000

INTRODUCTION: Glaucoma is an incurable eye syndrome and the second leading reason of vision loss. A retinal scan is usually used to detect it. Glaucoma poses a challenge to predict in its nascent stages because the side effects of glaucoma are not recognized until the advanced stages of the disease are reached. Therefore, regular eye examinations are important and recommended. Manual glaucoma screening methods are labour-intensive and time-consuming processes. However, deep learning-based glaucoma detection methods reduce the need for manual work and improve accuracy and speed.

OBJECTIVES: conduct a literature analysis of latest technical publications using various AI, Machine learning, and Deep learning methodologies for automated glaucoma detection.

RESULTS: There are 329 Scopus articles on glaucoma detection using retinal images. The quantitative review presented state-of-art methods from different research publications and articles and the usage of a fundus image database for qualitative and quantitative analysis. This paper presents the execution of Explainable AI for Glaucoma prediction Analysis. Explainable AI (XAI) is artificial intelligence (AI) that allows humans to understand AI decisions and predictions. This contrasts with the machine learning “black box” concept, where even the designer cannot explain why the AI made certain decisions. XAI is committed to improving user performance. To provide reliable explanations for Glaucoma forecasting from unhealthy and diseased photos, XAI primarily employs an Adaptive Neuro-fuzzy Inference System (ANFIS).

CONCLUSION: This article proposes and compares the performance metrics of ANFIS & SNN fuzzy layers, VGG19, AlexNet, ResNet, and MobileNet.

3D Convolutional Neural Networks for Predicting Protein Structure for Improved Drug Recommendation

Pokkuluri Kiran Sree, SSSN Usha Devi N — Mon, 08 Apr 2024 00:00:00 +0000

INTRODUCTION: Protein structure prediction is critical for recommendation personalized medicine and drug discovery. This paper introduces a robust approach using 3D Convolution Neural Networks (3D CNN’s) to improve the accuracy of the structure of protein structure thus contributing for the drug recommendation system.

OBJECTIVES: In contrast to conventional techniques, 3D CNNs are able to identify complicated folding patterns and comprehend the subtle interactions between amino acids because they are able to capture spatial dependencies inside protein structures.

METHODS: Data sets are collected from Protein Data Bank, including experimental protein structures and the drugs that interact with them, are used to train the model. With the efficient processing of three-dimensional data, the 3D CNNs exhibit enhanced capability in identifying minute structural details that are crucial for drug binding. This drug recommendation system novel method makes it easier to find potential drugs that interact well with particular protein structures.

RESULTS: The performance of the proposed classifier is compared with the existing baseline methods with various parameters accuracy, precision, recall, F1 score, mean squared error (MSE) and area under the receiver operating characteristic curve (AUC-ROC).

CONCLUSION: Deep learning and 3D structural insights work together to create a new generation of tailored and focused therapeutic interventions by speeding up the drug development process and improving the accuracy of pharmacological recommendations.

Enhancing Disease Diagnosis: Statistical Analysis of Haematological Parameters in Sickle Cell Patients, Integrating Predictive Analytics

Bhawna Dash, Soumyalatha Naveen, Ashwinkumar UM — Tue, 09 Apr 2024 00:00:00 +0000

Sickle cell disease (SCD) affects 30 million people worldwide, causing a range of symptoms from mild to severe, including Vaso occlusive crises (VOC). SCD leads to damaging cycles of sickling and desickling of red blood cells due to HbS polymer formation, resulting in chronic haemolytic anaemia and tissue hypoxia. We propose using machine learning to categorize SCD patients based on haemoglobin, reticulocyte count, and LDH levels, crucial markers of hemolysis. Statistical analysis, particularly Linear Regression, demonstrates how haemoglobin depletion occurs using LDH and reticulocyte parameters.

Bilirubin and haemoglobin, two integral biomarkers in clinical biochemistry and haematology, serve distinct yet interconnected roles in human physiology. Bilirubin, a product of heme degradation, is a critical indicator of liver function and various hepatic disorders, while haemoglobin, found in red blood cells, is responsible for oxygen transport throughout the body. Understanding the statistical relationship between these biomarkers has far-reaching clinical implications, enabling improved diagnosis, prognosis, and patient care. This research paper conducts a comprehensive statistical analysis of bilirubin and haemoglobin using various regression techniques to elucidate their intricate association. The primary objective of this study is to characterize the relationship between bilirubin and haemoglobin. Through meticulous data analysis, we explore whether these biomarkers exhibit positive, negative, or no correlation. Additionally, this research develops predictive models for estimating haemoglobin levels based on bilirubin data, offering valuable tools for healthcare professionals in clinical practice.

Exploring Mental Fatigue and Burnout in the Workplace: A Survival Analysis Approach

R Eswar Reddy, Santhi K — Wed, 10 Apr 2024 00:00:00 +0000

INTRODUCTION: Employee burnout is a prevalent concern in contemporary workplaces, adversely impacting both individual well-being and organizational productivity.

OBJECTIVES: In this paper, we conducted a comprehensive analysis of a dataset focusing on mental fatigue and burnout among employees, employing various survival analysis techniques including Kaplan Meier, Nelson-Aalen, and Cox proportional-hazards models, as well as Frailty Models and Competing Risks Analysis.

METHODS: This research underscored significant associations between mental fatigue, burnout, and adverse outcomes, emphasizing the critical need for early identification and intervention. Kaplan Meier analysis revealed differences in survival probabilities, while Nelson-Aalen analysis depicted cumulative hazard functions over time. Cox proportional-hazards models identified mental fatigue and burnout as significant predictors of adverse events, supported by Frailty Models accounting for individual-level variability. Additionally, Competing Risks Analysis elucidated the simultaneous occurrence of multiple adverse events among employees experiencing burnout.

RESULTS: This research underscored significant associations between mental fatigue, burnout, and adverse outcomes, emphasizing the critical need for early identification and intervention. Kaplan Meier analysis revealed differences in survival probabilities, while Nelson-Aalen analysis depicted cumulative hazard functions over time. Cox proportional-hazards models identified mental fatigue and burnout as significant predictors of adverse events, supported by Frailty Models accounting for individual-level variability. Additionally, Competing Risks Analysis elucidated the simultaneous occurrence of multiple adverse events among employees experiencing burnout.

CONCLUSION: This study contributes valuable insights into understanding and addressing mental fatigue in the workplace, highlighting the importance of evidence-based interventions to support employee well-being and organizational resilience. The insights gained from this analysis inform evidence-based strategies for mitigating burnout-related risks and promoting a healthier work environment.

In vitro chronic wound healing using collagen and plant extract along with zinc nanoparticles

J Sofia Bobby, S Purnima, V Mythily, B Ghiri Rajan, S Shubhankar, M Sowmiya — Mon, 15 Apr 2024 00:00:00 +0000

INTRODUCTION: While the use of zinc nanoparticles (ZnNPs) as an antibacterial agent in the biomedical industry has recently attracted significant attention, collagen has aroused significant interest as a biomaterial in medical and tissue engineering applications.

OBJECTIVES: In order to create biofilm loaded with biosynthesized ZnNPs for use in chronic wound healing applications, type-I collagen was extracted from the study's subject. by the acid soluble collagen technique, collagen was isolated from the fish skin of the trevally and identified by SDS-PAGE. Aqueous extract from Cassia fistula leaves was also used to greenly manufacture stable ZnNPs, which were then characterized by UV-Vis, FTIR, and XRD measurements.

METHODS: Collagen and ZnNPs were then added to polyvinyl alcohol (PVA), creating a thin biofilm that had a high biocompatibility due to the production method's absence of a chemical reducer and crosslinking agent. When tested against the harmful bacteria, both ZnNPs alone and PVA/Collagen/ZnNPs biofilms showed potent antibacterial activity.

RESULTS: By using the MTT test, the cytotoxic effects of collagen and ZnNPs on the Vero cell line were evaluated. With 97.76% wound closure, the PVA/Collagen/ZnNPs biofilm demonstrated strong in vitro wound scratch healing efficacy.

CONCLUSION: The findings show that the PVA/Collagen/ZnNPs film dramatically increased cell migration by 40.0% at 24 hours, 79.20% at 48 hours, and 97.76% at 74 hours.

Detection of Lung and Colon Cancer using Average and Weighted Average Ensemble Models

Mon, 05 Feb 2024 00:00:00 +0000

INTRODUCTION: Cancer is a life-threatening condition triggered by metabolic irregularities or the convergence of hereditary disorders. Cancerous cells in lung and colon leads more death rate count in the human race today. The histological diagnosis of malignant cancers is critical in establishing the most appropriate treatment for patients. Detecting cancer in its early stages, before it has a chance to advance within the body, greatly reduces the risk of death in both cases.

OBJECTIVES: In order to examine a larger patient group more efficiently and quickly, researchers can utilize different methods of machine learning approach and different models of deep learning used to speed up the detection of cancer.

METHODS: In this work, we provide a new ensemble transfer learning model for the rapid detection of lung and colon cancer. By ingtegrating various models of transfer learning approach and combining these methods in an ensemble, we aim to enhance the overall performance of the diagnosis process.

RESULTS: The outcomes of this research indicate that our suggested approach performs better than current models, making it a valuable tool for clinics to support medical personnel in more efficiently detecting lung and colon cancer.

CONCLUSION: The average ensemble is able to reach an accuracy of 98.66%, while the weighted-average ensemble with an accuracy of 99.80%, which is good with analysis of existing approaches.

A Comprehensive Feature Engineering Approach for Breast Cancer Dataset

Shambhvi Sharma, Monica Sahni — Thu, 07 Mar 2024 00:00:00 +0000

Breast cancer continues to pose a significant challenge in the field of healthcare, serving as the primary cause of cancer-related deaths in women on a global scale. The present study aims to investigate the intricate relationship between breast cancer, statistical analysis, and feature engineering. By conducting an extensive analysis of a comprehensive dataset and employing sophisticated statistical methodologies, this research endeavor aims to unveil concealed insights that can enrich the medical community's existing knowledge base. Through the implementation of rigorous feature selection and extraction methodologies, the overarching aim is to augment the comprehension of breast cancer. Moreover, the study showcases the successful incorporation of univariate and bivariate analysis in order to enhance the accuracy of diagnostic procedures. The convergence of these disciplines exhibits considerable promise in the realm of breast cancer detection and prediction, facilitating cooperative endeavours aimed at addressing this widespread malignancy.

A Deep Learning Framework for Prediction of Cardiopulmonary Arrest

Thu, 14 Mar 2024 00:00:00 +0000

INTRODUCTION: The cardiopulmonary arrest is a major issue in any country. Gone are the days when it used to happen to those who are aged but now it is a major concern emerging among adolescents as well. According to the World Health Organization (WHO), cardiac arrest and stroke is still a major concern and remains a public health crisis. In past years India has witnessed many cases of heart related issues which used to occur predominantly among people having high cholesterol. But now the scenario has changed, and cases have been observed in people having normal cholesterol levels. There are several factors involved in heart stroke such as age, sex, blood pressure, etc. which are used by doctors to monitor and diagnose the same.

OBJECTIVES: This paper focuses on different predictive models and ways to improve the accuracy of prediction by analyzing datasets on how they affect the accuracy of certain algorithms.

METHODS: The factors contributing to heart issues can be used as a beacon to predict the stroke and help an individual to further consult a doctor beforehand. The idea is to target the datasets and the prediction algorithms of deep learning including advanced ones to improvise it and attain a better result.

RESULTS: This paper brings out the comparative analysis among neural network techniques like ANN, Transfer Learning, MAML and LRP in which ANN showed the best result by giving the highest accuracy of 94%.

CONCLUSION: Furthermore, it discusses a new attribute called “gamma prime fibrinogen” which could be used in the future to boost prediction performance.

An LSTM based DNN Model for Neurological Disease Prediction Using Voice Characteristics

Anila M, G Kiran Kumar, D Malathi Rani, M V V Prasad Kantipudi, D Jayaram — Thu, 14 Mar 2024 00:00:00 +0000

INTRODUCTION: A neurological condition known as Parkinson's disease (PD); it affected millions of individuals worldwide. An early diagnosis can help enhance the quality of life for those who are affected with this disease. This paper presents a novel Deep neural network model based on Long Short-Term Memory (LSTM) design for the identification of PD using voice features.

OBJECTIVES: This research work aims to Identify the presence of PD using voice features of individuals. To achieve this, a Deep neural Network with LSTM is to be designed. Objective of the work is to analyse the voice data and implement the model with good accuracy.

METHODS: The proposed model is a Deep Neural Network with LSTM.

RESULTS: The proposed method uses the features gleaned from voice signals for training phase of LSTM model which achieved an accuracy of 89.23%, precision value as 0.898, F1-score of 0.965, and recall value as 0.931and is observed as best when compared to existing models.

CONCLUSION: Deep Neural Networks are more powerful than ANNs ahd when associated with LSTM , the model outperformed the job of identifying PD using voice data.

Application of Several Transfer Learning Approach for Early Classification of Lung Cancer

Fri, 15 Mar 2024 00:00:00 +0000

INTRODUCTION: Lung cancer, a fatal disease characterized by abnormal cell growth, ranks as the second most lethal worldwide, as observed in recent research conducted in India and other regions. Early detection is crucial for effective treatment, and manual differentiation of nodule types in CT images poses challenges for radiologists.

OBJECTIVES: To enhance accuracy and efficiency, deep learning algorithms are proposed for early lung cancer detection. Transfer learning-based computer recognition algorithms have shown promise in providing radiologists with additional insights.

METHODS: The dataset used in this study comprises 1000 CT scan images representing lung large cell carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, and normal lung cases. A preprocessing phase, including picture rescaling and modification, is applied to the input CT scan images of the lungs, followed by the utilization of a specific transfer learning model to develop a lung cancer detection system.

RESULTS: The performance of various transfer learning strategies is evaluated using measures such as accuracy, precision, recall, specificity, area under the curve, and F1-score.

CONCLUSION: Comparative analysis indicates that VGG16 outperforms other models in accurately categorizing different types of lung cancer.

Clinical Application of Neural Network for Cancer Detection Application

R Kishore Kanna, R Ravindraiah, C Priya, R Gomalavalli, Nimmagadda Muralikrishna — Mon, 18 Mar 2024 00:00:00 +0000

INTRODUCTION: The field of medical diagnostics is currently confronted with a significant obstacle in the shape of cancer, a disease that tragically results in the loss of millions of lives each year. Ensuring the administration of appropriate treatment to cancer patients is of paramount significance for medical practitioners.

OBJECTIVES: Hence, the accurate identification of cancer cells holds significant importance. The timely identification of a condition can facilitates prompt diagnosis and intervention. Numerous researchers have devised multiple methodologies for the early detection of cancer.

METHODS: The accurate anticipation of cancer has consistently posed a significant and formidable undertaking for medical professionals and researchers. This article examines various neural network technologies utilised in the diagnosis of cancer.

RESULTS: Neural networks have emerged as a prominent area of research within the medical science field, particularly in disciplines such as cardiology, radiology, and oncology, among others.

CONCLUSION: The findings of this survey indicate that neural network technologies demonstrate a high level of efficacy in the diagnosis of cancer. A significant proportion of neural networks exhibit exceptional precision when it comes to categorizing tumours cells.

CNN Based Face Emotion Recognition System for Healthcare Application

Mon, 18 Mar 2024 00:00:00 +0000

INTRODUCTION: Because it has various benefits in areas such psychology, human-computer interaction, and marketing, the recognition of facial expressions has gained a lot of attention lately.

OBJECTIVES: Convolutional neural networks (CNNs) have shown enormous potential for enhancing the accuracy of facial emotion identification systems. In this study, a CNN-based approach for recognizing facial expressions is provided. METHODS: To boost the model's generalizability, transfer learning and data augmentation procedures are applied. The recommended strategy defeated the existing state- of-the-art models when examined on multiple benchmark datasets, including the FER-2013, CK+, and JAFFE databases.

RESULTS: The results suggest that the CNN-based approach is fairly excellent at properly recognizing face emotions and has a lot of potential for usage in detecting facial emotions in practical scenarios.

CONCLUSION: Several diverse forms of information, including oral, textual, and visual, maybe applied to comprehend emotions. In order to increase prediction accuracy and decrease loss, this research recommended a deep CNN model for emotion prediction from facial expression.

Detection of Brain Tumour based on Optimal Convolution Neural Network

Tue, 19 Mar 2024 00:00:00 +0000

INTRODUCTION: Tumours are the second most frequent cause of cancer today. Numerous individuals are at danger owing to cancer. To detect cancers such as brain tumours, the medical sector demands a speedy, automated, efficient, and reliable procedure.

OBJECTIVES: Early phases of therapy are critical for detection. If an accurate tumour diagnosis is possible, physicians safeguard the patient from danger. In this program, several image processing algorithms are utilized.

METHODS: Utilizing this approach, countless cancer patients are treated, and their lives are spared. A tumor is nothing more than a collection of cells that proliferate uncontrolled. Brain failure is caused by the development of brain cancer cells, which devour all of the nutrition meant for healthy cells and tissues. Currently, physicians physically scrutinize MRI pictures of the brain to establish the location and size of a patient's brain tumour. This takes a large amount of time and adds to erroneous tumour detection.

RESULTS: A tumour is a development of tissue that is uncontrolled. Transfer learning may be utilized to detect the brain cancer utilizing. The model's capacity to forecast the presence of a cancer in a picture is its best advantage. It returns TRUE if a tumor is present and FALSE otherwise.

CONCLUSION: In conclusion, the use of CNN and deep learning algorithms to the identification of brain tumor has shown remarkable promise and has the potential to completely transform the discipline of radiology.

Machine Learning Based Assessment and Predictive Analysis of In-Vitro Fertilization Success Rate

Fri, 22 Mar 2024 00:00:00 +0000

INTRODUCTION: The transformation in the lifestyle and other societal and economic factors during modern times have led to rise in the cases of infertility among young generation. Apart from these factors infertility may also be attributed to different medical conditions among both men and women. This rise in the cases of infertility is a matter of huge concern to the mankind and should be seriously pondered upon. However, the unprecedented advancements in the field of healthcare have led to In Vitro fertilization as a rescue to this devastating condition. Although the In Vitro fertilization has the potential to unfurl the happiness, it has associated challenges also in terms of physical and emotional health. Also, the success rate of In Vitro fertilization may vary from person to person.

OBJECTIVES: To predict the success rate of In Vitro fertilization.

METHODS: Machine Learning Models.

RESULTS: It has been observed that Adaboost outperforms all other machine learning models by yielding an accuracy of 97.5%.

CONCLUSION: During the result analysis, it is concluded that if age > 36, there is a negative propensity for clinical pregnancy and if age >40, the probability of a clinical pregnancy dramatically declines. Further, the propensity of clinical pregnancy is positively correlated to the count of embryos transferred in the same IVF cycle.

Machine Learning based Exploratory Data Analysis (EDA) and Diagnosis of Chronic Kidney Disease (CKD)

Fri, 22 Mar 2024 00:00:00 +0000

INTRODUCTION: This research paper presents an exploratory data analysis (EDA) approach to diagnose Chronic Kidney Disease (CKD) using machine learning algorithms.

OBJECTIVES: This paper focuses on early and accurate detection of CKD using a comprehensive dataset of clinical and laboratory parameters to minimize the risk of patients’ health complications with timely intervention through appropriate medications.

METHODS: Machine Learning based prediction models including Naive Bayes, KNN, Logistic regression, decision tree, ensemble modelling, Random Forest and Ada Boost.

RESULTS: The results indicate that the Naive Bayes algorithm achieved highest accuracy and sensitivity in detecting CKD.

CONCLUSION: For reduced features and for binary class classification, Naive Bayes classifier gives best performance in terms of accuracy and computational cost. Other algorithms are good for multi-class classification but for binary class, they are little expensive than Naive Bayes.

Depressonify: BERT a deep learning approach of detection of depression

Meena Kumari, Gurpreet Singh, Sagar Dhanraj Pande — Fri, 22 Mar 2024 00:00:00 +0000

INTRODUCTION: Depression is one of the leading psychological problems in the modern tech era where every single person has a social media account that has wide space for the creation of depressed feelings. Since depression can escalate to the point of suicidal thoughts or behavior spotting it early can be vitally important. Traditionally, psychologists rely on patient interviews and questionnaires to gauge the severity of depression.

OBJECTIVES: The objective of this paper is earlier depression detection as well as treatment can greatly improve the probability of living a healthy and full life free of depression.

METHODS: This paper introduces the utilization of BERT, a novel deep-learning, transformers approach that can detect levels of depression using textual data as input.

RESULTS: The main result obtained in this paper is the extensive dataset consists of a total of 20,000 samples, which are categorized into 5 classes and further divided into training, testing, and validation sets, with respective sizes of 16,000, 2,000, and 2,000. This paper has achieved a remarkable result with a training accuracy of 95.5% and validation accuracy of 92.2% with just 5 epochs.

CONCLUSION: These are the conclusions of this paper, Deep learning has a lot of potential for use in mental health applications, as seen by the study's outstanding results, which included training accuracy of 95.5%. But the path towards comprehensive and morally sound AI-based mental health support continues into the future.

Modelling of Diabetic Cases for Effective Prevalence Classification

Shrey Shah, Monika Mangla, Nonita Sharma, Tanupriya Choudhury, Maganti Syamala — Fri, 22 Mar 2024 00:00:00 +0000

INTRODUCTION: This study compares and contrasts various machine learning algorithms for predicting diabetes. The study of current research work is to analyse the effectiveness of various machine learning algorithms for diabetes prediction.

OBJECTIVES: To compare the efficacy of various machine learning algorithms for diabetic prediction.

METHODS: For the same, a diabetic dataset was subjected to the application of various well-known machine learning algorithms. Unbalanced data was handled by pre-processing the dataset. The models were subsequently trained and assessed using different performance metrics namely F1-score, accuracy, sensitivity, and specificity.

RESULTS: The experimental results show that the Decision Tree and ensemble model outperforms all other comparative models in terms of accuracy and other evaluation metrics.

CONCLUSION: This study can help healthcare practitioners and researchers to choose the best machine learning model for diabetes prediction based on their specific needs and available data.

Effective Cataract Identification System using Deep Convolution Neural Network

P N Senthil Prakash, S Sudharson, Venkat Amith Woonna, Sai Venkat Teja Bacham — Fri, 22 Mar 2024 00:00:00 +0000

INTRODUCTION: The paper introduces a novel approach for the early detection of cataracts using images captured using smartphones. Cataracts are a significant global eye disease that can lead to vision impairment in individuals aged 40 and above. In this article, we proposed a deep convolution neural network (CataractsNET) trained using an open dataset available in Github which includes images collected through google searches and images generated using standard augmentation mechanism.

OBJECTIVES: The main objective of this paper is to design and implement a lightweight network model for cataract identification that outperforms other state-of-the-art network models in terms of accuracy, precision, recall, and F1 Score.

METHODS: The proposed neural network model comprises nine layers, guaranteeing the extraction of significant details from the input images and achieving precise classification. The dataset primarily comprises cataract images sourced from a standardized dataset that is publicly available on GitHub, with 8000 training images and 1600 testing images.

RESULTS: The proposed CataractsNET model achieved an accuracy of 96.20%, precision of 96.1%, recall of 97.6%, and F1 score of 96.1%. These results demonstrate that the proposed method outperforms other deep learning models like ResNet50 and VGG19.

CONCLUSION: The paper concludes that identifying cataracts in the earlier stages is crucial for effective treatment and reducing the likelihood of experiencing blindness. The widespread use of smartphones makes this approach accessible to a broad audience, allowing individuals to check for cataracts and seek timely consultation with ophthalmologists for further diagnosis.

Comparative Analysis of Polycystic Ovary Syndrome Detection Using Machine Learning Algorithms

Neha Yadav, Ranjith Kumar A, Sagar Dhanraj Pande — Tue, 26 Mar 2024 00:00:00 +0000

INTRODUCTION: Polycystic Ovary Syndrome is a condition in which the ovaries manufacture androgen, seen in small traces, resulting in the production of cysts. Menstrual cycle abnormalities, clinical and/or biochemical hyperandrogenism, and the presence of polycystic ovaries on ultrasound should all be used to diagnose PCOS. PCOS appears to be a multifaceted illness influenced by both genetic and environmental factors and the symptoms include excessive hair on the face and body, weight gain, voice changes, skin type changes, and irregular periods.

OBJECTIVES: This is the objective of this paper is to identify PCOS in its initial stage.

METHODS: To address this issue the study proposes a comparison of various machine learning algorithms and optimization techniques Among which GSCV gave the best result of 94% accuracy, followed by TPOT with 91% accuracy. Additionally, we also applied Feature selection methods to eliminate zero-importance features to increase the accuracy of algorithms.

RESULTS: The main results obtained in this paper This study explored various Feature selection techniques, ML and DL models. It is shown that Grid Search CV and TPOT classifier were best classifiers with 94% and 91% respectively.

CONCLUSION: These are the conclusions of this paper and this study will explore various DL methodologies and try to find out best optimal results for the PCOS Detection. And also, to develop an PCOS detection application to keep track of menstrual cycles and track activities and symptoms for PCOS.

Brain Tumor Detection and Classification Using Deep Learning Models on MRI Scans

L Chandra Sekhar Reddy, Muniyandy Elangovan, M Vamsikrishna, Ch Ravindra — Tue, 26 Mar 2024 00:00:00 +0000

INTRODUCTION: The primary goal of artificial intelligence (AI) is to develop computers that exhibit human-like behavior and functionality. Computer-based activities employing artificial intelligence encompass a variety of extra features beyond only pattern detection, planning, and problem resolution.

METHODOLOGY: Machines use a set of techniques collectively called "deep learning." Magnetic resonance imaging (MRI) is employed with the use of deep learning methods to develop models that can effectively identify and classify brain cancers. This technique facilitates the rapid and straightforward detection of brain cancers. Brain problems mainly arise from the abnormal multiplication of brain cells, leading to detrimental alterations in brain structure and finally culminating in the development of cancer in the brain, malignant. Early detection of brain tumors along with following effective intervention can reduce mortality rates. This paper proposes convolutional neural network (CNN) architecture to effectively detect brain cancers using magnetic resonance (MR) images.

RESULTS: This research further examines several models, including ResNet-50, VGG16, and Inception V3, and compares the proposed architecture and these models. For the efficacy of the models, many measures were evaluated, including accuracy, recall, loss, and area under the curve (AUC). After analyzing several models and comparing them with the suggested model using the specified metrics, it was determined that the proposed model exhibited superior performance compared to the alternative models. Based on an analysis conducted on data from 3265 MR images.

CONCLUSION: It was seen that the CNN model exhibited a classification precision of 93.3%. Additionally, the area under the receiver operating characteristic curve (AUC) was determined to be 98.43%, while the recall rate was 91.19%. Furthermore, the model's loss function yielded a value of 0.25. Based on a comparative analysis with other models, it can be inferred that the suggested model is highly reliable in detecting various types of brain cancers at an early stage.

Predicting and Propagation of Diabetic Foot Infection by Deep Learning Model

Rajanish Kumar Kaushal, P R Panduraju Pagidimalla, C Nalini, Devendra Kumar — Tue, 02 Apr 2024 00:00:00 +0000

INTRODUCTION: A deep learning model may be used to predict the occurrence of diabetic foot infections and to understand how these infections spread over time by using sophisticated machine learning methods. Untreated diabetic foot infections, a common diabetic complication, may have devastating effects.

METHODOLOGY: One area where deep learning models—a kind of machine learning—shine is in healthcare, where they are well-suited to deal with data that contains intricate patterns and correlations. The metabolic illness of diabetes affects more individuals than any other. Neuropathic and Ischemic ulcers are two types of foot ulcers that these issues may cause. Damage to the nerves and blood vessels is the primary cause of this ulcer. Numerous amputations and fatalities have resulted from these sores. There are millions of victims of this illness throughout the globe. The amputation of a human leg occurs once every 30 seconds. The precise anticipation of diabetic foot ulcers has the potential to significantly alleviate the substantial impact of amputation Therefore, it is crucial to correctly categorize foot ulcers and discover them as soon as possible for more effective treatment.

RESULTS: An extensive literature review of classification methods, including decision trees, random forests, the M5 tree method, Random trees, neural network models, ZeroR, Naive Bayes, the Back Propagation Neural Network, Linear Regression model, and Deep Learning Algorithms is presented in this research with a primary emphasis on foot ulcer classification. Using the Kaggle dataset, these algorithms are ranked. In the end, it presents a comparison of different classifiers.

Speckle Noise Removal from Biomedical MRI Images and Classification by Multi-Support Vector Machine

B Hemalatha, B Karthik, C V Krishna Reddy — Thu, 08 Feb 2024 00:00:00 +0000

INTRODUCTION: Image Processing (IP) methods play a vital role in medical images for diagnosing and predicting illness, as well as monitoring the patient's progress. The IP methods are utilized in many applications for example in the field of medicine.

OBJECTIVES: The images that are obtained by the MRI magnetic Resonance imaging and x rays are analyzed with the help of image processing.

METHODS: This application is very costly to the patient. Because of the several non-idealities in the image process, medical images are frequently tainted by impulsive, multiplicative, and addictive noise.

RESULTS: By replacing some of the original image's pixels with new ones that have luminance values which are less than the allowed dynamic luminance range, noise frequently affects medical images.

CONCLUSION: In this research work, the Speckle type noises are eliminated with the help of Mean Filter (MF) and classify the images using Multi-SVM classifier. The entire system developed using python programming.

Graphical image of Trisomy Ultrascan related Total edge magic labelling

A Pradeepa, O V Shanmuga Sundaram, N Pushpalatha — Wed, 06 Mar 2024 00:00:00 +0000

INTRODUCTION: The goal of this research is to investigate child syndromes at the overall level using total edge magic labelling. luckily discussed with chromosomal diseases consisting of Down's syndrome, the syndrome of Edwards, and Patau syndrome.

OBJECTIVES: Ultrasound is used to check for Patau's, Edwards, and Down syndrome between 11 and 14 weeks of pregnancy. These syndromes can be determined before the baby is born. The name for trisomy 21 or Down syndrome. Trisomy 18 or Edwards syndrome; trisomy 13 or Patau syndrome.

METHODS: The ultrasound screen test was converted to a graphical image, and Total edge magic labelling was implemented. A bijection from VUE to the numbers, {1, 2, 3, … p+q} with the characteristic that each everybody uv Ɛ E, Γ(u)+ Γ(uv)+ Γ(v) = Ψ for some constant Ψ, is known as Total edge magic labelling.

RESULTS: The results of this test will determine the baby’s type of trisomy. This study's impartial was to assess the efficacy of screening for 21, 18, and 13 trisomies at the 12-week mark in pregnancy.

CONCLUSION: The intended audience of this paper is a man or woman with a chromosomal disorder who should know about the health of their ancestors. A couple can go for genetic counselling and then plan for a baby.

A predictive prototype for the identification of diseases relied on the symptoms described by patients

Suvendu Kumar Nayak, Mamata Garanayak, Sangram Keshari Swain — Wed, 13 Mar 2024 00:00:00 +0000

INTRODUCTION: A thorough and timely investigation of any health-related problem is essential for disease prevention and treatment. The normal way of diagnosis may not be sufficient in the event of a serious illness problem.

OBJECTIVE: Creating a medical diagnosis prototype that uses many machine learning processes to forecast any illness relied on symptoms explained by patients can lead to an errorless diagnosis as compared to the traditional ways.

METHODS: We created a disease prediction prototype using ML techniques such as random forest, CART, multinomial linear regression, and KNN. The data set utilized for processing contained over 132 illnesses. Diagnosis algorithm outcomes the ailment that the person may be suffering from relied on the symptoms provided by the patients.

RESULTS: When compared to CART and random forest (accuracy is 97.72%, multinomial linear regression and KNN produced the best outcomes. The accuracy of the KNN prediction and multinomial linear regression techniques was 98.76%.

CONCLUSION: The diagnostic prototype can function as a doctor in the early detection of an illness, ensuring that medical care can begin in an appropriate time and many lives can be secured.

Anxiety Controlling Application using EEG Neurofeedback System

Fri, 15 Mar 2024 00:00:00 +0000

INTRODUCTION: This study aims to investigate the correlation between the oscillations of electroencephalography (EEG) bands and the level of anxiety in a sample of sixteen youth athletes aged 17–21. The research utilizes a mobile EEG system to collect data on EEG band oscillations.

OBJECTIVES: The aim of this research study is to investigate the brain wave oscillations during relaxation, specifically comparing the contrast between eyes open and eyes closed state Electroencephalography (EEG) using a state-of-the-art wireless EEG headset system.

METHODS: The system incorporates dry, non-interacting EEG sensor electrodes, developed exclusively by NeuroSky. In addition, the addition of the ThinkGear module and MindCap XL skull facilitated EEG recording. The aim of the present study was to investigate the effect of eyes open and eyes closed conditions on alpha-band activity in the prefrontal cortex The results showed a statistically significant difference (p≤0.006); appeared between these two states. The present study examined the relationship between the alpha band of the prefrontal cortex and anxiety levels. Specifically, we examined the relationship between these variables in the eyes-closed condition.

RESULTS: Our analysis revealed a statistically significant correlation, with the alpha band showing a negative slope (p≤0.029). The present study examines the comparison of data obtained from single-channel wireless devices with data obtained from conventional laboratories The findings of this study show a striking similarity between the results obtained with both types of devices. The aim of the present study was to investigate the specific characteristics of the correlation between electroencephalographic (EEG) alphaband oscillations in the prefrontal cortex in relation to eye position and anxiety levels in young athletes.

CONCLUSION: This study seeks to shed light on the possible relationship between this vibration and individuals' internal cognitive and affective states.

Automated Life Stage Classification of Malaria Using Deep Learning

Fri, 15 Mar 2024 00:00:00 +0000

INTRODUCTION: Malaria, an infectious illness spread by mosquitoes, is a serious hazard to humans and animals, with an increasing number of cases recorded yearly. Prompt and precise diagnosis, as well as preventative actions, are critical for effectively combating this condition. Malaria is now diagnosed using standard techniques. Microscopy of blood smears, which consists of small pictures, is used by trained specialists to identify diseased cells and define their life phases. The World Health Organisation (WHO) has approved this microscopy-based malaria diagnostic method. Drawing a blood sample from the finger, pricking it, spreading it onto a clean glass slide, and allowing it to dry naturally are all steps in the method. Thin blood smears were previously used to identify parasites under the microscope, but thick blood smears are utilized when parasite levels are low.

OBJECTIVES: Due to its reliance on medical knowledge, high prices, time-consuming nature, and unsatisfactory outcomes, this technique has significant disadvantages. However, as deep learning algorithms progress, these activities may be completed more effectively and with fewer human resources.

METHODS: This study demonstrates the usefulness of transfer learning, a type of deep learning, in categorizing microscopic pictures of parasitized versus uninfected malaria cells. Six models were evaluated using the publicly accessible NIH dataset, proving the usefulness of the suggested technique.

RESULTS: VGG19 model fared better than its competitors, obtaining 95.05% accuracy, 92.83% precision, 96.88% sensitivity, 93.46% specificity, and 94.81% F1-score.

CONCLUSION: This categorization of malaria cell photos will benefit microscopists in particular, as it will improve their workflow and provide a viable alternative for detecting malaria using microscopic cell images.

Integrated Embedded system for detecting diabetes mellitus using various machine learning techniques

Thu, 21 Mar 2024 00:00:00 +0000

INTRODUCTION: The goal of this study, titled ”Integrated System for Detecting Diabetes Mellitus using Various Machine Learning and Deep Learning Algorithms,” is to increase the precision and usability of diabetes diagnosis through the investigation and application of a wide range of machine learning and deep learning techniques.

OBJECTIVES: The objective of the study was to establish a comprehensive system for identifying diabetes mellitus by combining several machine learning and deep learning methods

METHODS: The methodology included every phase, from data gathering and preprocessing through advanced model development and performance assessment. The experiment demonstrated how combining several machine learning and deep learning techniques might completely transform diabetes detection. While praising accomplishments, the methodology also highlighted flaws in the data collection process. The goal of the roadmap for future improvements was to use technology to better detect and treat diabetes, which would ultimately help people of all ages and backgrounds.

RESULTS: The project’s remarkable results demonstrate the legitimacy of the methodology chosen while also highlighting its potential to completely transform the diagnosis and treatment of diabetes

CONCLUSION: The conclusion of this project lays the ground for next developments, such as improved user interfaces and the expansion of dataset scope. Through these initiatives, the long-term objective of providing more precise and accessible diabetes diagnoses becomes a real possibility, providing significant advantages to people from a variety of age groups and demographics[6].

An Integrated Thresholding and Morphological Process with Histogram-based Method for Brain Tumor Analysis and MRI Tumor Detection

Thu, 21 Mar 2024 00:00:00 +0000

INTRODUCTION: Over the past several years analysis of image has moved from larger system to pervasive portable devices. For example, in pervasive biomedical systems like PACS-Picture achieving and Communication system, computing is the main element. Image processing application for biomedical diagnosis needs efficient and fast algorithms and architecture for their functionality. Future pervasive systems designed for biomedical application should provide computational efficiency and portability. The discrete wavelet transform (DWT) designed in on-chip been used in several applications like data, audio signal processing and machine learning.

OBJECTIVES: The conventional convolution based scheme is easy to implement but occupies more memory , power and delay. The conventional lifting based architecture has multiplier blocks which increase the critical delay. Designing the wavelet transform without multiplier is a effective task especially for the 2-D image analysis. Without multiplier Daubechies wavelet implementation in forward and inverse transforms may find efficient. The objective of the work is on obtaining low power and less delay architecture.

METHODS: The proposed lifting scheme for two dimensional architecture reduces critical path through multiplier less and provides low power, area and high throughput. The proposed multiplier is delay efficient.

RESULTS: The architecture is Multiplier less in the predict and update stage and the implementation carried out in FPGA by the use of Quartus II 9.1 and it is found that there is reduction in consumption of power at approximately 56%. There is reduction in delay due to multiplier less architecture.

CONCLUSION: multiplier less architecture provides less delay and low power. The power observed is in milliwatts and suitable for high speed application due to low critical path delay.

Deep Learning Framework for Liver Tumor Segmentation

Wed, 27 Mar 2024 00:00:00 +0000

INTRODUCTION: Segregating hepatic tumors from the liver in computed tomography (CT) scans is vital in hepatic surgery planning. Extracting liver tumors in CT images is complex due to the low contrast between the malignant and healthy tissues and the hazy boundaries in CT images. Moreover, manually detecting hepatic tumors from CT images is complicated, time-consuming, and needs clinical expertise.

OBJECTIVES: An automated liver and hepatic malignancies segmentation is essential to improve surgery planning, therapy, and follow-up evaluation. Therefore, this study demonstrates the creation of an intuitive approach for segmenting tumors from the liver in CT scans.

METHODS: The proposed framework uses residual UNet (ResUNet) architecture and local region-based segmentation. The algorithm begins by segmenting the liver, followed by malignancies within the liver envelope. First, ResUNet trained on labeled CT images predicts the coarse liver pixels. Further, the region-level segmentation helps determine the tumor and improves the overall segmentation map. The model is tested on a public 3D-IRCADb dataset.

RESULTS: Two metrics, namely dice coefficient and volumetric overlap error (VOE), were used to evaluate the performance of the proposed method. ResUNet model achieved dice of 0.97 and 0.96 in segmenting liver and tumor, respectively. The value of VOE is also reduced to 1.90 and 0.615 for liver and tumor segmentation.

CONCLUSION: The proposed ResUNet model performs better than existing methods in the literature. Since the proposed model is built using U-Net, the model ensures quality and precise dimensions of the output.

A Comparative Analysis using various algorithm Approaches to Enhance Heart Disease Prognosis

Tue, 02 Apr 2024 00:00:00 +0000

INTRODUCTION: Modern advancements in technology and data science have propelled the healthcare industry towards developing more accurate disease prognostic prediction models. Heart disease, being a leading cause of mortality globally, is a critical area of focus. This study delves into enhancing heart disease prognosis through a comprehensive exploration of various algorithmic approaches.

OBJECTIVES: The objective of this paper is to compare and analyze different algorithmic techniques to improve heart disease prognosis using a dataset comprising data from over thirty thousand individuals obtained through Kaggle.

METHODS: Techniques derived from social network analysis are employed to conduct this research. Data preprocessing, feature engineering, algorithm selection (including Stochastic Gradient Descent, AdaBoosting, Support Vector Machine, and Naive Bayes), hyperparameter tuning, model evaluation, and visualization are part of the systematic research process.

RESULTS: The main results obtained in this paper include the identification of Naive Bayes as the most effective model for heart disease prognosis, followed by AdaBoosting, SVM, and Stochastic Gradient Descent. Performance evaluation metrics such as AUC, CA, F1, Precision, and Recall demonstrate the efficacy of these models.

CONCLUSION: This research contributes to improving heart disease prognosis by leveraging algorithmic techniques and thorough analysis. The study envisions integrating the developed model into healthcare systems for widespread access to accurate heart disease prediction, with future plans to enhance data collection and model improvement for better outcomes.

A Survey on Impact of Internet of Medical Things Against Diabetic Foot Ulcer

R. Athi Vaishnavi, P Jegathesh, M Jayasheela, K Mahalakshmi — Wed, 21 Feb 2024 00:00:00 +0000

INTRODUCTION: In this study, we explore the intricate domain of Diabetic Foot Ulcers (DFU) through the development of a comprehensive framework that encompasses diverse operational scenarios. The focus lies on the identification and classification assessment of diabetic foot ulcers, the implementation of smart health management strategies, and the collection, analysis, and intelligent interpretation of data related to diabetic foot ulcers. The framework introduces an innovative approach to predicting diabetic foot ulcers and their key characteristics, offering a technical solution for forecasting. The exploration delves into various computational strategies designed for intelligent health analysis tailored to patients with diabetic foot ulcers.

OBJECTIVES: The primary objective of this paper is to present a technical solution for forecasting diabetic foot ulcers, utilizing computational strategies for intelligent health analysis.

METHODS: Techniques derived from social network analysis are employed to conduct this research, focusing on diverse computational strategies geared towards intelligent health analysis for patients with diabetic foot ulcers. The study highlights methodologies addressing the unique challenges posed by diabetic foot ulcers, with a central emphasis on the integration of Internet of Medical Things (IoMT) in prediction strategies.

RESULTS: The main results of this paper include the proposal of IoMT-based computing strategies covering the entire spectrum of DFU analysis, such as localization, classification assessment, intelligent health management, and detection. The study also acknowledges the challenges faced by previous research, including low classification rates and elevated false alarm rates, and proposes automatic recognition approaches leveraging advanced machine learning techniques to enhance accuracy and efficacy.

CONCLUSION: The proposed IoMT-based computing strategies present a significant advancement in addressing the challenges associated with predicting diabetic foot ulcers. The integration of advanced machine learning techniques demonstrates promise in improving accuracy and efficiency in diabetic foot ulcer localization, marking a positive stride towards overcoming existing limitations in previous research.

A Comprehensive Exploration of Artificial Intelligence Methods for COVID-19 Diagnosis

Balasubramaniam S, Arishma M, Satheesh Kumar K, Rajesh Kumar Dhanaraj — Wed, 21 Feb 2024 00:00:00 +0000

INTRODUCTION: The 2019 COVID-19 pandemic outbreak triggered a previously unseen global health crisis demanding accurate diagnostic solutions. Artificial Intelligence has emerged as a promising technology for COVID-19 diagnosis, offering rapid and reliable analysis of medical data.

OBJECTIVES: This research paper presents a comprehensive review of various artificial intelligence methods applied for the diagnosis, aiming to assess their effectiveness in identifying cases, predicting disease progression and differentiating from other respiratory diseases.

METHODS: The study covers a wide range of artificial intelligence methods and with application in analysing diverse data sources like chest x-rays, CT scans, clinical records and genomic sequences. The paper also explores the challenges and limitations in implementing AI -based diagnostic tools, including data availability and ethical considerations.

CONCLUSION: Leveraging AI’s potential in healthcare can significantly enhance diagnostic efficiency crisis management as the pandemic evolves.

Lung Cancer Detection in CT Images Using Deep Learning Techniques: A Survey Review

C Usharani, B Revathi, A Selvapandian, S K Kezial Elizabeth — Fri, 01 Mar 2024 00:00:00 +0000

INTRODUCTION: The Computed Tomography (CT) imaging-based Lung cancer detection is crucial for early diagnosis. This survey paper presents an overview of the techniques and advancements in CT-based lung cancer detection. It covers the fundamentals of CT imaging, including principles, types, and protocols.

OBJECTIVES: The paper explores image processing techniques for pre-processing, such as noise reduction, enhancement, and segmentation.

METHODS: Additionally, it discusses feature extraction methods, including shape, texture, and intensity-based features, as well as Deep Learning (DL) and Machine Learning (ML) methods for automated classification.

RESULTS: Computerised systems and their integration is examined with CT imaging along with performance evaluation metrics. The survey concludes by addressing challenges, limitations, and future directions. The imaging modalities and artificial intelligence techniques are used to improve lung cancer detection.

CONCLUSION: This comprehensive survey aims to provide a concise understanding of CT-based lung cancer detection for researchers and healthcare professionals.

A Review on the Importance of Machine Learning in the Health-Care Domain

Thu, 07 Mar 2024 00:00:00 +0000

INTRODUCTION: An analysis of the convergence of blockchain and artificial intelligence (AI) technology demonstrates how these technologies can work together to revolutionize data management across a wide range of industries with their synergistic potential.

OBJECTIVES: This paper discusses the integration of blockchain and artificial intelligence, the authors present an innovative framework that takes advantage of their strengths. As a result of blockchain's immutability and transparency, data can be securely stored and shared within this framework, making it ideal for sectors such as healthcare, finance, and supply chain.

METHODS: To begin with, the paper discusses blockchain and artificial intelligence individually, emphasizing their respective advantages in decentralized data storage and intelligent decision-making. Blockchain-AI convergence is inevitable as both deal with data and value.

RESULTS: As a result, the research paper highlights how blockchain and AI technologies can be transformed into transformative technologies.

CONCLUSION: Using the synergistic framework presented in this paper, data management can be made more secure, transparent, and intelligent, with implications that go beyond traditional industries into emerging fields like the Internet of Things (IoT) and smart cities.

A Review: Machine Learning and Data Mining Approaches for Cardiovascular Disease Diagnosis and Prediction

Gorapalli Srinivasa Rao, G Muneeswari — Wed, 13 Mar 2024 00:00:00 +0000

INTRODUCTION: Cardiovascular disease (CVD) is the most common cause of death worldwide, and its prevalence is rising in low-resource settings and among those with lower incomes.

OBJECTIVES: Machine learning (ML) algorithms are quickly evolving and being implemented in medical procedures for CVD diagnosis and treatment decisions. Every day, the healthcare business creates massive amounts of data. However, the majority of it is inadequately utilized. Efficient techniques for extracting knowledge from these datasets for clinical diagnosis or other uses are scarce.

METHODS: ML is being applied in the healthcare industry all over the world. In the health dataset, ML approaches useful in the prevention of locomotor disorders and heart disease.

RESULTS: The revelation of such vital information allows researchers to acquire significant insight into how to use the proper treatment and diagnosis for a specific patient. Researchers study enormous volumes of complex healthcare data using various ML approaches, which improves healthcare professionals in disease prediction.

CONCLUSION: The goal of this study is to summarize some of the current research on predicting heart diseases utilizing machine learning and data mining techniques, analyze the various mining algorithm combinations employed, and determine which techniques are useful and efficient. Future directions in prediction systems have also been considered.