Comparative Analysis of Transfer Learning Models for Breast Cancer Classification

This repository contains the code and resources for the paper:

Comparative Analysis of Transfer Learning Models for Breast Cancer Classification
Authors: Sania Eskandari, Ali Eslamian, Qiang Cheng
Published: 2024 (arXiv preprint arXiv:2408.16859)

Overview

This repository provides the code used to evaluate various transfer learning models for breast cancer classification, as described in our paper. The study compares multiple deep learning architectures and highlights performance metrics across different models applied to histopathology images.

Models Evaluated:

• ResNet-50
• DenseNet-121
• Vision Transformer (ViT)
• ResNeXt-50
• GoogLeNet (Inception v3)
• EfficientNet
• MobileNet
• SqueezeNet

Dataset:

• The dataset used in this study can be found on Kaggle: Breast Histopathology Images

Citation

If you use this code or reference our study in your work, please cite:

@article{eskandari2024comparative,
  title={Comparative Analysis of Transfer Learning Models for Breast Cancer Classification},
  author={Eskandari, Sania and Eslamian, Ali and Cheng, Qiang},
  journal={arXiv preprint arXiv:2408.16859},
  year={2024}
}