Review of AlexNet for Medical Image Classification

TL;DR

This paper reviews the impact of AlexNet on medical image classification, highlighting its technical innovations like dropout and ReLU that advanced CNN development and applications in the field.

Contribution

It provides a comprehensive review of AlexNet's technical details, advantages, and its influence on medical image classification after analyzing over 40 related papers.

Findings

AlexNet introduced dropout to reduce overfitting.

ReLU activation helped avoid gradient vanishing.

AlexNet significantly advanced CNN applications in medical imaging.

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.