Research Article
Review of AlexNet for Medical Image Classification
@ARTICLE{10.4108/eetel.4389, author={Wenhao Tang and Junding Sun and Shuihua Wang and Yudong Zhang}, title={Review of AlexNet for Medical Image Classification}, journal={EAI Endorsed Transactions on e-Learning}, volume={9}, number={1}, publisher={EAI}, journal_a={EL}, year={2023}, month={12}, keywords={Medical Image Classification, ReLU, Neural Networks, Gradient Vanishing, CNNs}, doi={10.4108/eetel.4389} }- Wenhao Tang
Junding Sun
Shuihua Wang
Yudong Zhang
Year: 2023
Review of AlexNet for Medical Image Classification
EL
EAI
DOI: 10.4108/eetel.4389
Abstract
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.
Copyright © 2023 W. Tang et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
