  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.
