Clinical Application of Neural Network for Cancer Detection Application

Authors

  • R Kishore Kanna Jerusalem College of Engineering
  • R Ravindraiah Madanapalle Institute of Technology & Science
  • C Priya Siddarth Institute of Engineering and Technology
  • R Gomalavalli Siddarth Institute of Engineering and Technology
  • Nimmagadda Muralikrishna PACE Institute of Technology and Sciences

DOI:

https://doi.org/10.4108/eetpht.10.5454

Keywords:

Cancer, Neural Network, Cells, ML

Abstract

 

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.

Downloads

Download data is not yet available.

References

Khanyile, R., Marima, R., Mbeje, M., Mutambirwa, S., Montwedi, D., & Dlamini, Z. (2023). AI Tools Offering Cancer Clinical Applications for Risk Predictor, Early Detection, Diagnosis, and Accurate Prognosis: Perspectives in Personalised Care. In Artificial Intelligence and Precision Oncology: Bridging Cancer Research and Clinical Decision Support (pp. 293-312). Cham: Springer Nature Switzerland. DOI: https://doi.org/10.1007/978-3-031-21506-3_15

Chaudhury, S., & Sau, K. (2023). A blockchain-enabled internet of medical things system for breast cancer detection in healthcare. Healthcare Analytics, 100221. DOI: https://doi.org/10.1016/j.health.2023.100221

Illimoottil, M., & Ginat, D. (2023). Recent Advances in Deep Learning and Medical Imaging for Head and Neck Cancer Treatment: MRI, CT, and PET Scans. Cancers, 15(13), 3267. DOI: https://doi.org/10.3390/cancers15133267

Kanna, R. Kishore, N. Kripa, and R. Vasuki. "Systematic Design of Lie Detector System Utilizing EEG Signals Acquisition." International Journal of Scientific & Technology Research 9: 610-2.

Fu, S., Xu, J., Chang, S., Yang, L., Ling, S., Cai, J., ... & Zhao, Q. (2023). Robust vascular segmentation for raw complex images of laser speckle contrast based on weakly supervised learning. IEEE Transactions on Medical Imaging. DOI: https://doi.org/10.1109/TMI.2023.3287200

Tabatabaei, S., Rezaee, K., & Zhu, M. (2023). Attention transformer mechanism and fusion-based deep learning architecture for MRI brain tumor classification system. Biomedical Signal Processing and Control, 86, 105119. DOI: https://doi.org/10.1016/j.bspc.2023.105119

Kanna, R. K., & Vasuki, R. (2019). Advanced Study of ICA in EEG and Signal Acquisition using Mydaq and Lab view Application. International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN, 2278-3075.

Patel, G. K., Verma, S., Misra, S., Chand, G., & Rao, R. N. Molecular drivers of prostate cancer pathogenesis and therapy resistance. Frontiers in Cell and Developmental Biology, 11, 1239478. DOI: https://doi.org/10.3389/fcell.2023.1239478

Prasath Alias Surendhar, S., Kanna, R. K., & Indumathi, R. (2023). Ensemble Feature Extraction with Classification Integrated with Mask RCNN Architecture in Breast Cancer Detection Based on Deep Learning Techniques. SN Computer Science, 4(5), 618. DOI: https://doi.org/10.1007/s42979-023-01893-z

Ravikumar, K. K., Ishaque, M., Panigrahi, B. S., & Pattnaik, C. R. (2023). Detection of Covid-19 Using AI Application. EAI Endorsed Transactions on Pervasive Health and Technology, 9. DOI: https://doi.org/10.4108/eetpht.9.3349

Kanna, R.K., Banappagoudar, S.B., Menezes, F.R., Sona, P.S. (2023). Patient Monitoring System for COVID Care Using Biosensor Application. In: Tomar, R.S., et al. Communication, Networks and Computing. CNC 2022. Communications in Computer and Information Science, vol 1893. Springer, Cham. https://doi.org/10.1007/978-3-031-43140-1_27 DOI: https://doi.org/10.1007/978-3-031-43140-1_27

Kripa, N., Vasuki, R., & Kanna, R. K. (2019). Realtime neural interface controlled au-pair BIMA bot. International Journal of Recent Technology and Engineering, 8(1), 992-4.

Kanna, R. Kishore, et al. "Smart Electronic Arm Module using Arduino Applications." 2022 IEEE International Conference on Current Development in Engineering and Technology (CCET). IEEE, 2022. DOI: https://doi.org/10.1109/CCET56606.2022.10080068

Liew, A., Agaian, S., & Zhao, L. (2023, June). Mitigation of adversarial noise attacks on skin cancer detection via ordered statistics binary local features. In Multimodal Image Exploitation and Learning 2023 (Vol. 12526, pp. 153-164). SPIE. DOI: https://doi.org/10.1117/12.2664239

Jebakumar, S., Hemalatha, R.J., Kanna, R.K. (2023). GSM Enabled Patient Monitoring System Using Arduino Application for Cardiac Support. In: Nandan Mohanty, S., Garcia Diaz, V., Satish Kumar, G.A.E. (eds) Intelligent Systems and Machine Learning. ICISML 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 470. Springer, Cham. DOI: https://doi.org/10.1007/978-3-031-35078-8_24

R. K. Kanna, R. Chandrasekaran, A. A. Khafel, M. Brayyich, K. A.Jabbar and H. Al-Chlidi, "Study On Diabetic Conditions Monitoring Using Deep Learning Application," 2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), Greater Noida, India, 2023, pp. 363-366. DOI: https://doi.org/10.1109/ICACITE57410.2023.10183002

Thomas, L., & M. K, S. (2023). Fourier Ptychographic and Deep Learning using Breast Cancer Histopathological Image Classification. Journal of Biophotonics, e202300194. DOI: https://doi.org/10.1002/jbio.202300194

R. K. Kanna, S. Prasath Alias Surendhar, M. R. AL-Hameed, A. M. Lafta, R. Khalid and A. Hussain, "Smart Prosthetic Arm Using Cognitive Application," 2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), Greater Noida, India, 2023, pp. 1330-1334. DOI: https://doi.org/10.1109/ICACITE57410.2023.10182794

Sarpe, C., Ciobotea, E. R., Morscher, C. B., Zielinski, B., Braun, H., Senftleben, A., ... & Baumert, T. (2023). Identification of tumor tissue in thin pathological samples via femtosecond laser-induced breakdown spectroscopy and machine learning. Scientific Reports, 13(1), 9250. DOI: https://doi.org/10.1038/s41598-023-36155-8

Kanna, R. K., Ansari, A. A., Kripa, N., Jyothi, G., Mutheeswaran, U., & Hema, L. K. (2022, December). Automated Defective ECG Signal Detection using MATLAB Applications. In 2022 IEEE International Conference on Current Development in Engineering and Technology (CCET) (pp. 1-7). IEEE.

Mohanty, S., Nayak, D. S. K., & Swarnkar, T. (2023, June). A neural network framework for predicting Adenocarcinoma cancer using high-throughput gene expression data. In AIP Conference Proceedings (Vol. 2819, No. 1). AIP Publishing. DOI: https://doi.org/10.1063/5.0137033

R. K. Kanna, U. Mutheeswaran, A. J. Jouda, M. Asaad Hussein, A. Hussain and M. Al- Tahee, "Computational Cognitive Analysis of ADHD Patients using Matlab Applications," 2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), Greater Noida, India, 2023, pp. 1344-1348

Wang, Y., Hu, H., Yu, S., Yang, Y., Guo, Y., Song, X., ... & Liu, Q. (2023). A unified hybrid transformer for joint MRI sequences super-resolution and missing data imputation. Physics in Medicine and Biology. DOI: https://doi.org/10.1088/1361-6560/acdc80

R. K. Kanna and R. Vasuki, "Advanced BCI applications for detection of drowsiness state using EEG waveforms", Materials Today: Proceedings, 2021. DOI: https://doi.org/10.1016/j.matpr.2021.01.784

Shanmuga Raju, S., Paulchamy, B., Rajarajeswari, K., & Nithyadevi, S. (2023). Future of Medicine in Cognitive Technologies and Automatic Detection via Computational Techniques. In Translating Healthcare Through Intelligent Computational Methods (pp. 373-393). Cham: Springer International Publishing. DOI: https://doi.org/10.1007/978-3-031-27700-9_23

R. K. Kanna, U. Mutheeswaran, A. J. Jouda, M. Asaad Hussein, A. Hussain and M. Al-Tahee, "Computational Cognitive Analysis of ADHD Patients using Matlab Applications," 2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), Greater Noida, India, 2023, pp. 1344-1348. DOI: https://doi.org/10.1109/ICACITE57410.2023.10182435

Rubi J, A. V, kanna KR, G. U. Bringing Intelligence to Medical Devices Through Artificial Intelligence. Advances in Medical Technologies and Clinical Practice. 2023 Jan 13;154–68. DOI: https://doi.org/10.4018/978-1-6684-6434-2.ch007

Kanna, R. Kishore, et al. "Automated Defective ECG Signal Detection using MATLAB Applications." 2022 IEEE International Conference on Current Development in Engineering and Technology (CCET). IEEE, 2022. DOI: https://doi.org/10.1109/CCET56606.2022.10080386

Prasath, S., et al. "Hearing loss analysis using audiometry." Drug Invention Today 11.7 (2019).

Mohapatra, Srikanta Kumar, et al. "Systematic Stress Detection in CNN Application." 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO). IEEE, 2022. DOI: https://doi.org/10.1109/ICRITO56286.2022.9964761

Downloads

Published

18-03-2024

How to Cite

1.
Kishore Kanna R, Ravindraiah R, Priya C, Gomalavalli R, Muralikrishna N. Clinical Application of Neural Network for Cancer Detection Application. EAI Endorsed Trans Perv Health Tech [Internet]. 2024 Mar. 18 [cited 2024 Apr. 25];10. Available from: https://publications.eai.eu/index.php/phat/article/view/5454