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.

翻译：近年来，深度学习的快速发展推动了其在医学图像分类领域的广泛应用。性能不断提升的神经网络模型变体具有一些共同特点：试图缓解过拟合、提高泛化能力、避免梯度消失与梯度爆炸等问题。AlexNet首次采用dropout技术来缓解过拟合，并使用ReLU激活函数避免梯度消失。因此，本文将重点探讨在2012年对CNN发展做出重大贡献的AlexNet。在综述超过40篇论文（包括期刊论文和会议论文）的基础上，本文系统阐述了AlexNet的技术细节、优势及其应用领域。