Review of AlexNet for Medical Image Classification
DOI:
https://doi.org/10.4108/eetel.4389Keywords:
Medical Image Classification, ReLU, Neural Networks, Gradient Vanishing, CNNsAbstract
In recent years, the rapid development of deep learning has led to a wide range of applications in the field of medical image classification. The variants of neural network models with ever-increasing performance share some commonalities: to try to mitigate overfitting, improve generalization, avoid gradient vanishing and exploding, etc. AlexNet first utilizes the dropout technique to mitigate overfitting and the ReLU activation function to avoid gradient vanishing. Therefore, we focus our discussion on AlexNet, which has contributed greatly to the development of CNNs in 2012. After reviewing over 40 papers, including journal papers and conference papers, we give a narrative on the technical details, advantages, and application areas of AlexNet.
References
M. Minsky and S. A. Papert, Perceptrons: An Introduction to Computational Geometry: The MIT Press, 2017.
Y. Yan, R. Chen, Z. H. Yang, Y. Ma, J. L. Huang, L. Luo, et al., "Application of back propagation neural network model optimized by particle swarm algorithm in predicting the risk of hypertension," JOURNAL OF CLINICAL HYPERTENSION, vol. 24, pp. 1606-1617, 2022.
M. Roder, L. A. Passos, G. H. de Rosa, V. H. C. de Albuquerque, and J. P. Papa, "Reinforcing learning in Deep Belief Networks through nature-inspired optimization," APPLIED SOFT COMPUTING, vol. 108, Article ID: 107466, 2021.
N. Yu, Z. Yu, F. Gu, T. Li, X. Tian, and Y. Pan, "Deep Learning in Genomic and Medical Image Data Analysis: Challenges and Approaches," Journal of Information Processing Systems, vol. 13, pp. 204-214, 2017.
S. Dong, P. Wang, and K. Abbas, "A survey on deep learning and its applications," Computer Science Review, vol. 40, p. 100379, 2021.
K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," IEEE, 2016.
C. Garbin, X. Zhu, and O. Marques, "Dropout vs. batch normalization: an empirical study of their impact to deep learning," Multimedia Tools and Applications, vol. 79, pp. 12777-12815, 2020.
F. Kong, F. Q. Liu, K. D. Xu, and X. S. Shi, "Why does batch normalization induce the model vulnerability on adversarial images?," WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS, vol. 26, pp. 1073-1091, 2023.
C. Y. Low, J. Park, and A. B. J. Teoh, "Stacking-Based Deep Neural Network: Deep Analytic Network for Pattern Classification," IEEE TRANSACTIONS ON CYBERNETICS, vol. 50, pp. 5021-5034, 2020.
G. E. Hinton and R. R. Salakhutdinov, "Reducing the Dimensionality of Data with Neural Networks," Science, vol. 313, pp. 504-507, 2006.
Z. Ren, S. Wang, and Y. Zhang, "Weakly supervised machine learning," CAAI Transactions on Intelligence Technology, vol. 8, pp. 549-580, 2023.
S. Mozaffari, O. Y. Al-Jarrah, M. Dianati, P. Jennings, and A. Mouzakitis, "Deep Learning-Based Vehicle Behavior Prediction for Autonomous Driving Applications: A Review," IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, vol. 23, pp. 33-47, 2022.
A. Krizhevsky, I. Sutskever, and G. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks," Advances in neural information processing systems, vol. 25, 2012.
S. Lu, Z. Lu, and Y.-D. Zhang, "Pathological brain detection based on AlexNet and transfer learning," Journal of Computational Science, vol. 30, pp. 41-47, 2019.
L. Alzubaidi, J. L. Zhang, A. J. Humaidi, A. Al-Dujaili, Y. Duan, O. Al-Shamma, et al., "Review of deep learning: concepts, CNN architectures, challenges, applications, future directions," JOURNAL OF BIG DATA, vol. 8, Article ID: 53, 2021.
Y. D. Zhang, S. C. Satapathy, D. S. Guttery, J. M. Górriz, and S. H. Wang, "Improved Breast Cancer Classification Through Combining Graph Convolutional Network and Convolutional Neural Network," INFORMATION PROCESSING & MANAGEMENT, vol. 58, Article ID: 102439, 2021.
M. Varshney and P. Singh, "Optimizing nonlinear activation function for convolutional neural networks," SIGNAL IMAGE AND VIDEO PROCESSING, vol. 15, pp. 1323-1330, 2021.
D. Boob, S. S. Dey, and G. Lan, "Complexity of training ReLU neural network," Discrete Optimization, vol. 44, p. 100620, 2022.
S. Ioffe and C. Szegedy, "Batch normalization: accelerating deep network training by reducing internal covariate shift," in Proceedings of the 32nd International Conference on International Conference on Machine Learning - Volume 37, Lille, France, 2015, pp. 448–456.
A. Bin Tufail, I. Ullah, A. U. Rehman, R. A. Khan, M. A. Khan, Y. K. Ma, et al., "On Disharmony in Batch Normalization and Dropout Methods for Early Categorization of Alzheimer's Disease," SUSTAINABILITY, vol. 14, Article ID: 14695, 2022.
V. Jain, P. Gupta, A. Chaudhry, M. Batra, and D. J. Hemanth, "A Modified Deep Convolution Siamese Network for Writer-Independent Signature Verification," INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS, vol. 30, pp. 479-498, 2022.
K. He, X. Zhang, S. Ren, and J. Sun, "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification," in Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, 2015, pp. 1026–1034.
D.-A. Clevert, T. Unterthiner, and S. Hochreiter, "Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)," arXiv: Learning, 2015.
P. Ramachandran, B. Zoph, and Q. V. Le, "Swish: a Self-Gated Activation Function," arXiv: Neural and Evolutionary Computing, 2017.
A. P. Piotrowski, J. J. Napiorkowski, and A. E. Piotrowska, "Impact of deep learning-based dropout on shallow neural networks applied to stream temperature modelling," EARTH-SCIENCE REVIEWS, vol. 201, Article ID: 103076, 2020.
G. E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. R. Salakhutdinov. (2012). Improving neural networks by preventing co-adaptation of feature detectors. arXiv:1207.0580. doi: 10.48550/arXiv.1207.0580
G. Ghiasi, T.-Y. Lin, and Q. V. Le, "DropBlock: a regularization method for convolutional networks," in Proceedings of the 32nd International Conference on Neural Information Processing Systems, Montréal, Canada, 2018, pp. 10750–10760.
I. Salehin and D. K. Kang, "A Review on Dropout Regularization Approaches for Deep Neural Networks within the Scholarly Domain," ELECTRONICS, vol. 12, Article ID: 3106, 2023.
M. D. Zeiler and R. Fergus, "Visualizing and Understanding Convolutional Networks," ArXiv, vol. abs/1311.2901, 2013.
K. Simonyan and A. Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition," CoRR, vol. abs/1409.1556, 2014.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, and A. Rabinovich, "Going Deeper with Convolutions," IEEE Computer Society, 2014.
F. N. Iandola, M. W. Moskewicz, K. Ashraf, S. Han, W. J. Dally, and K. Keutzer, "SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size," ArXiv, vol. abs/1602.07360, 2016.
G. Huang, Z. Liu, L. Maaten, and K. Q. Weinberger, "Densely Connected Convolutional Networks," in IEEE Conference on Computer Vision and Pattern Recognition, 2017.
Y. Zhang, V. Govindaraj, C. Tang, W. Zhu, and J. Sun, "High Performance Multiple Sclerosis Classification by Data Augmentation and AlexNet Transfer Learning Model," J. Medical Imaging Health Informatics, vol. 9, pp. 2012-2021, 2019.
T. M. Ghazal, S. Munir, S. Abbas, A. Athar, H. Alrababah, and M. A. Khan, "Early Detection of Autism in Children Using Transfer Learning," INTELLIGENT AUTOMATION AND SOFT COMPUTING, vol. 36, pp. 11-22, 2023.
T. Balashanmugam, K. Sengottaiyan, M. S. Kulandairaj, and H. Dang, "An effective model for the iris regional characteristics and classification using deep learning alex network," IET IMAGE PROCESSING, vol. 17, pp. 227-238, 2023.
I. Kayadibi, G. E. Güraksin, U. Ergün, and N. Süzme, "An Eye State Recognition System Using Transfer Learning: AlexNet-Based Deep Convolutional Neural Network," INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS, vol. 15, Article ID: 49, 2022.
R. Girshick, "Fast R-CNN," in 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, 2015, pp. 1440-1448.
S. Ren, K. He, R. Girshick, and J. Sun, "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, pp. 1137-1149, 2017.
S. Kiziloluk and E. Sert, "Hurricane-Faster R-CNN-JS: Hurricane detection with faster R-CNN using artificial Jellyfish Search (JS) optimizer," MULTIMEDIA TOOLS AND APPLICATIONS, vol. 81, pp. 37981-37999, 2022.
A. Singh, V. Kalaichelvi, and R. Karthikeyan, "Performance analysis of object detection algorithms for robotic welding applications in planar environment," INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING, vol. 36, pp. 1083-1108, 2023.
S. Y. Xie, C. S. Hu, M. Bagavathiannan, and D. Z. Song, "Toward Robotic Weed Control: Detection of Nutsedge Weed in Bermudagrass Turf Using Inaccurate and Insufficient Training Data," IEEE ROBOTICS AND AUTOMATION LETTERS, vol. 6, pp. 7365-7372, 2021.
G. M. Li, Y. B. Huang, Z. Q. Chen, G. D. Chesser, J. L. Purswell, J. Linhoss, et al., "Practices and Applications of Convolutional Neural Network-Based Computer Vision Systems in Animal Farming: A Review," SENSORS, vol. 21, Article ID: 1492, 2021.
C. S. Pereira, R. Morais, and M. Reis, "Deep Learning Techniques for Grape Plant Species Identification in Natural Images," SENSORS, vol. 19, Article ID: 4850, 2019.
Y. H. Bu, X. N. Jiang, J. P. Tian, X. J. Hu, L. P. Han, D. Huang, et al., "Rapid nondestructive detecting of sorghum varieties based on hyperspectral imaging and convolutional neural network," JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE, vol. 103, pp. 3970-3983, 2023.
G. B. Ergün and S. Güney, "Classification of Canine Maturity and Bone Fracture Time Based on X-Ray Images of Long Bones," IEEE ACCESS, vol. 9, pp. 109004-109011, 2021.
M. Talo, O. Yildirim, U. B. Baloglu, G. Aydin, and U. R. Acharya, "Convolutional neural networks for multi-class brain disease detection using MRI images," COMPUTERIZED MEDICAL IMAGING AND GRAPHICS, vol. 78, Article ID: 101673, 2019.
M. Sharma, R. K. Patel, A. Garg, R. SanTan, and U. R. Acharya, "Automated detection of schizophrenia using deep learning: a review for the last decade," PHYSIOLOGICAL MEASUREMENT, vol. 44, Article ID: 03tr01, 2023.
A. Shalbaf, S. Bagherzadeh, and A. Maghsoudi, "Transfer learning with deep convolutional neural network for automated detection of schizophrenia from EEG signals," PHYSICAL AND ENGINEERING SCIENCES IN MEDICINE, vol. 43, pp. 1229-1239, 2020.
Mohebbanaaz, L. V. R. Kumar, and Y. P. Sai, "A new transfer learning approach to detect cardiac arrhythmia from ECG signals," SIGNAL IMAGE AND VIDEO PROCESSING, vol. 16, pp. 1945-1953, 2022.
M. A. Berwo, Y. Fang, J. Mahmood, N. Yang, Z. J. Liu, and Y. M. Li, "FAECCD-CNet: Fast Automotive Engine Components Crack Detection and Classification Using ConvNet on Images," APPLIED SCIENCES-BASEL, vol. 12, Article ID: 9713, 2022.
S. Sharma and R. Mehra, "Effect of layer-wise fine-tuning in magnification-dependent classification of breast cancer histopathological image," VISUAL COMPUTER, vol. 36, pp. 1755-1769, 2020.
S. Y. Arafat, N. Ashraf, M. J. Iqbal, I. Ahmad, S. Khan, and J. Rodrigues, "Urdu signboard detection and recognition using deep learning," MULTIMEDIA TOOLS AND APPLICATIONS, vol. 81, pp. 11965-11987, 2022.
Q. Xu and Z. Y. Wang, "A Data-Driven Model for Automated Chinese Word Segmentation and POS Tagging," COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, vol. 2022, Article ID: 7622392, 2022.
B. Peng, H. B. Zhang, N. Yang, and J. M. Xie, "Vehicle Recognition from Unmanned Aerial Vehicle Videos Based on Fusion of Target Pre-Detection and Deep Learning," SUSTAINABILITY, vol. 14, Article ID: 7912, 2022.
M. Rafiq, G. Rafiq, R. Agyeman, G. S. Choi, and S. I. Jin, "Scene Classification for Sports Video Summarization Using Transfer Learning," SENSORS, vol. 20, Article ID: 1702, 2020.
R. A. Minhas, A. Javed, A. Irtaza, M. T. Mahmood, and Y. B. Joo, "Shot Classification of Field Sports Videos Using AlexNet Convolutional Neural Network," APPLIED SCIENCES-BASEL, vol. 9, Article ID: 483, 2019.
A. Mumtaz, A. B. Sargano, and Z. Habib, "Fast Learning Through Deep Multi-Net CNN Model For Violence Recognition In Video Surveillance," COMPUTER JOURNAL, vol. 65, 2022.
Irfanullah, T. Hussain, A. Iqbal, B. L. Yang, and A. Hussain, "Real time violence detection in surveillance videos using Convolutional Neural Networks," MULTIMEDIA TOOLS AND APPLICATIONS, vol. 81, pp. 38151-38173, 2022.
A. A. Khan, M. A. Nauman, M. Shoaib, R. Jahangir, R. Alroobaea, M. Alsafyani, et al., "Crowd Anomaly Detection in Video Frames Using Fine-Tuned AlexNet Model," ELECTRONICS, vol. 11, Article ID: 3105, 2022.
W. Imen, M. Amna, B. Fatma, S. F. Ezahra, and N. Masmoudi, "Fast HEVC intra-CU decision partition algorithm with modified LeNet-5 and AlexNet," SIGNAL IMAGE AND VIDEO PROCESSING, vol. 16, pp. 1811-1819, 2022.
A. J. Hung, R. Bao, I. O. Sunmola, D. A. Huang, J. H. Nguyen, and A. Anandkumar, "Capturing fine-grained details for video-based automation of suturing skills assessment," INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY, vol. 18, pp. 545-552, 2023.
N. Ziafat, H. F. Ahmad, I. Fatima, M. Zia, A. Alhumam, and K. Rajpoot, "Correct Pronunciation Detection of the Arabic Alphabet Using Deep Learning," APPLIED SCIENCES-BASEL, vol. 11, Article ID: 2508, 2021.
I. Attri, L. K. Awasthi, T. P. Sharma, and P. Rathee, "A review of deep learning techniques used in agriculture," ECOLOGICAL INFORMATICS, vol. 77, Article ID: 102217, 2023.
M. Alencastre-Miranda, R. M. Johnson, and H. I. Krebs, "Convolutional Neural Networks and Transfer Learning for Quality Inspection of Different Sugarcane Varieties," IEEE Transactions on Industrial Informatics, vol. 17, pp. 787-794, 2021.
M. A. Khan, T. Akram, M. Sharif, M. Awais, K. Javed, H. Ali, et al., "CCDF: Automatic system for segmentation and recognition of fruit crops diseases based on correlation coefficient and deep CNN features," COMPUTERS AND ELECTRONICS IN AGRICULTURE, vol. 155, pp. 220-236, 2018.
J. Zhang, L. He, M. Karkee, Q. Zhang, X. Zhang, and Z. M. Gao, "Branch detection for apple trees trained in fruiting wall architecture using depth features and Regions-Convolutional Neural Network (R-CNN)," COMPUTERS AND ELECTRONICS IN AGRICULTURE, vol. 155, pp. 386-393, 2018.
Z. Tarek, M. Elhoseny, M. I. Alghamdi, and I. M. El-Hasnony, "Leveraging three-tier deep learning model for environmental cleaner plants production," SCIENTIFIC REPORTS, vol. 13, Article ID: 19499, 2023.
L. Wang, S. B. Li, C. Q. Teng, C. Jiang, J. Y. Li, Z. Li, et al., "Automatic-Detection Method for Mining Subsidence Basins Based on InSAR and CNN-AFSA-SVM," SUSTAINABILITY, vol. 14, Article ID: 13898, 2022.
J. Ren, S. Zhou, J. Wang, S. Yang, and C. Liu, "Research on Identification of Natural and Unnatural Earthquake Events Based on AlexNet Convolutional Neural Network," Wireless Communications and Mobile Computing, vol. 2022, p. 6782094, 2022.
L. Ichim and D. Popescu, "Segmentation of Vegetation and Flood from Aerial Images Based on Decision Fusion of Neural Networks," REMOTE SENSING, vol. 12, Article ID: 2490, 2020.
S. Manoj and C. Valliyammai, "Drone network for early warning of forest fire and dynamic fire quenching plan generation," EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING, vol. 2023, Article ID: 112, 2023.
S. Lu, S.-H. Wang, and Y.-D. Zhang, "Detection of abnormal brain in MRI via improved AlexNet and ELM optimized by chaotic bat algorithm," Neural Computing and Applications, vol. 33, pp. 10799-10811, 2021.
P. Kora, C. P. Ooi, O. Faust, U. Raghavendra, A. Gudigar, W. Y. Chan, et al., "Transfer learning techniques for medical image analysis: A review," BIOCYBERNETICS AND BIOMEDICAL ENGINEERING, vol. 42, pp. 79-107, 2022.
Y.-D. Zhang, S. C. Satapathy, D. S. Guttery, J. M. Górriz, and S.-H. Wang, "Improved Breast Cancer Classification Through Combining Graph Convolutional Network and Convolutional Neural Network," Information Processing & Management, vol. 58, p. 102439, 2021.
M. A. Morid, A. Borjali, and G. Del Fiol, "A scoping review of transfer learning research on medical image analysis using ImageNet," COMPUTERS IN BIOLOGY AND MEDICINE, vol. 128, Article ID: 104115, 2021.
F. E. AlTahhan, G. A. Khouqeer, S. Saadi, A. Elgarayhi, and M. Sallah, "Refined Automatic Brain Tumor Classification Using Hybrid Convolutional Neural Networks for MRI Scans," DIAGNOSTICS, vol. 13, Article ID: 864, 2023.
M. Ramya, G. Kirupa, and A. Rama, "Brain tumor classification of magnetic resonance images using a novel CNN-based medical image analysis and detection network in comparison with AlexNet," Journal of population therapeutics and clinical pharmacology = Journal de la therapeutique des populations et de la pharmacologie clinique, vol. 29, pp. e97-e108, 2022.
A. Sarkar, M. Maniruzzaman, M. A. Alahe, and M. Ahmad, "An Effective and Novel Approach for Brain Tumor Classification Using AlexNet CNN Feature Extractor and Multiple Eminent Machine Learning Classifiers in MRIs," JOURNAL OF SENSORS, vol. 2023, Article ID: 1224619, 2023.
S. P. Singh, L. P. Wang, S. Gupta, H. Goli, P. Padmanabhan, and B. Gulyás, "3D Deep Learning on Medical Images: A Review," SENSORS, vol. 20, Article ID: 5097, 2020.
J. Alyami, A. Rehman, T. Sadad, M. Alruwaythi, T. Saba, and S. A. Bahaj, "Automatic skin lesions detection from images through microscopic hybrid features set and machine learning classifiers," MICROSCOPY RESEARCH AND TECHNIQUE, vol. 85, pp. 3600-3607, 2022.
S. B. Melingi, R. K. Mojjada, V. K. Reddy, C. Kumar, and K. A. Kumar, "A bio-inspired AlexNet-DrpXLm architype for an effective brain stroke lesion detection and classification," CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, vol. 34, Article ID: e7100, 2022.
I. Shafi, M. Sajad, A. Fatima, D. G. Aray, V. Lipari, I. D. Diez, et al., "Teeth Lesion Detection Using Deep Learning and the Internet of Things Post-COVID-19," SENSORS, vol. 23, Article ID: 6837, 2023.
V. K. Bairagi, P. P. Gumaste, S. H. Rajput, and K. S. Chethan, "Automatic brain tumor detection using CNN transfer learning approach," MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, vol. 61, pp. 1821-1836, 2023.
S. Petscharnig and K. Schöffmann, "Learning laparoscopic video shot classification for gynecological surgery," MULTIMEDIA TOOLS AND APPLICATIONS, vol. 77, pp. 8061-8079, 2018.
Y. Kumar and S. Gupta, "Deep Transfer Learning Approaches to Predict Glaucoma, Cataract, Choroidal Neovascularization, Diabetic Macular Edema, DRUSEN and Healthy Eyes: An Experimental Review," ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING, vol. 30, pp. 521-541, 2023.
P. Kumar, D. Suganthi, K. Valarmathi, M. P. Swain, P. Vashistha, D. Buddhi, et al., "A Multi-Thresholding-Based Discriminative Neural Classifier for Detection of Retinoblastoma Using CNN Models," BioMed research international, vol. 2023, pp. 5803661-5803661, 2023.
M. J. Umer, M. Sharif, M. Raza, and S. Kadry, "A deep feature fusion and selection-based retinal eye disease detection from OCT images," EXPERT SYSTEMS, vol. 40, 2023.
R. Aziz ur, I. A. Taj, M. Sajid, and K. S. Karimov, "An ensemble framework based on Deep CNNs architecture for glaucoma classification using fundus photography," MATHEMATICAL BIOSCIENCES AND ENGINEERING, vol. 18, pp. 5321-5346, 2021.
Y. M. Chen, W. T. Huang, W. H. Ho, and J. T. Tsai, "Classification of age-related macular degeneration using convolutional-neural-network-based transfer learning," BMC BIOINFORMATICS, vol. 22, Article ID: 99, 2021.
A. Ben Hamida, M. Devanne, J. Weber, C. Truntzer, V. Derangere, F. Ghiringhelli, et al., "Deep learning for colon cancer histopathological images analysis," COMPUTERS IN BIOLOGY AND MEDICINE, vol. 136, Article ID: 104730, 2021.
Q. Abbas, M. Albathan, A. Altameem, R. S. Almakki, and A. Hussain, "Deep-Ocular: Improved Transfer Learning Architecture Using Self-Attention and Dense Layers for Recognition of Ocular Diseases," DIAGNOSTICS, vol. 13, Article ID: 3165, 2023.
G. C. Y. Chan, R. Kamble, H. Muller, S. A. A. Shah, T. B. Tang, and F. Meriaudeau, "Fusing Results of Several Deep Learning Architectures for Automatic Classification of Normal and Diabetic Macular Edema in Optical Coherence Tomography," Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, vol. 2018, pp. 670-673, 2018.
X. Han, Z. Hu, S. Wang, and Y. Zhang, "A Survey on Deep Learning in COVID-19 Diagnosis," Journal of imaging, vol. 9, 2022.
P. Sabitha and G. Meeragandhi, "A dual stage AlexNet-HHO-DrpXLM archetype for an effective feature extraction, classification and prediction of liver cancer based on histopathology images," BIOMEDICAL SIGNAL PROCESSING AND CONTROL, vol. 77, Article ID: 103833, 2022.
A. Vijayasankar, S. F. Ahamed, B. Ramakrishna, N. U. Kumar, and B. Raju, "CNSD-Net: joint brain-heart disorders identification using remora optimization algorithm-based deep Q neural network," SOFT COMPUTING, vol. 27, pp. 12653-12668, 2023.
A. H. Al-Timemy, N. H. Ghaeb, Z. M. Mosa, and J. Escudero, "Deep Transfer Learning for Improved Detection of Keratoconus using Corneal Topographic Maps," COGNITIVE COMPUTATION, vol. 14, pp. 1627-1642, 2022.
M. H. Al-Adhaileh, "Diagnosis and classification of Alzheimer's disease by using a convolution neural network algorithm," SOFT COMPUTING, vol. 26, pp. 7751-7762, 2022.
E. Cortés and S. Sánchez, "Deep Learning Transfer with AlexNet for chest X-ray COVID-19 recognition," IEEE LATIN AMERICA TRANSACTIONS, vol. 19, pp. 944-951, 2021.
M. Chen, P. Zhou, D. Wu, L. Hu, M. M. Hassan, and A. Alamri, "AI-Skin: Skin disease recognition based on self-learning and wide data collection through a closed-loop framework," INFORMATION FUSION, vol. 54, pp. 1-9, 2020.
G. X. Wei, Y. Y. Liu, X. W. Ji, Q. X. Li, Y. Xing, Y. L. Xue, et al., "Micro-morphological feature visualization, auto-classification, and evolution quantitative analysis of tumors by using SR-PCT," CANCER MEDICINE, vol. 10, pp. 2319-2331, 2021.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 EAI Endorsed Transactions on e-Learning
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
This is an open-access article distributed under the terms of the Creative Commons Attribution CC BY 4.0 license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.
