Review of AlexNet for Medical Image Classification
This is an incremental review paper summarizing AlexNet's impact on CNNs for medical imaging applications.
This paper reviews AlexNet's contributions to medical image classification, highlighting its use of dropout to mitigate overfitting and ReLU to avoid gradient vanishing, based on an analysis of over 40 papers.
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.