# Review of AlexNet for Medical Image Classification

arXiv: https://arxiv.org/abs/2311.08655 · HTML: https://mlsanity.com/paper/2311.08655/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 101 references that are in the map.
- First posted on arXiv 15 Nov 2023.

## Builds on (most cited first, 19 of 19)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://mlsanity.com/paper/1602.07360/) (2016)
- [DropBlock: A regularization method for convolutional networks](https://mlsanity.com/paper/1810.12890/) (2018)
- [Searching for Activation Functions](https://mlsanity.com/paper/1710.05941/) (2017)
- [3D Deep Learning on Medical Images: A Review](https://mlsanity.com/paper/2004.00218/) (2020)
- [A scoping review of transfer learning research on medical image analysis using ImageNet](https://mlsanity.com/paper/2004.13175/) (2020)
- [Complexity of Training ReLU Neural Network](https://mlsanity.com/paper/1809.10787/) (2018)
- [Stacking-Based Deep Neural Network: Deep Analytic Network for Pattern Classification](https://mlsanity.com/paper/1811.07184/) (2018)
- [Toward Robotic Weed Control: Detection of Nutsedge Weed in Bermudagrass Turf Using Inaccurate and Insufficient Training Data](https://mlsanity.com/paper/2106.08897/) (2021)
- [AI-Skin : Skin Disease Recognition based on Self-learning and Wide Data Collection through a Closed Loop Framework](https://mlsanity.com/paper/1906.01895/) (2019)

## Abstract (arXiv)

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.