Deep Transfer Learning for Breast Cancer Classification

TL;DR

This paper explores the application of deep transfer learning techniques, specifically VGG, ViT, and ResNet, to classify breast cancer images, highlighting ResNet-34's superior accuracy and VGG-16's higher F1-score for improved diagnosis.

Contribution

It provides a comparative analysis of different deep transfer learning models for breast cancer classification, emphasizing ResNet-34's accuracy and VGG-16's F1-score advantages.

Findings

ResNet-34 achieved 90.40% accuracy in classifying IDC images.

VGG-16 demonstrated a higher F1-score due to fewer trainable parameters.

Transfer learning can enhance breast cancer screening accuracy with limited data.

Abstract

Breast cancer is a major global health issue that affects millions of women worldwide. Classification of breast cancer as early and accurately as possible is crucial for effective treatment and enhanced patient outcomes. Deep transfer learning has emerged as a promising technique for improving breast cancer classification by utilizing pre-trained models and transferring knowledge across related tasks. In this study, we examine the use of a VGG, Vision Transformers (ViT) and Resnet to classify images for Invasive Ductal Carcinoma (IDC) cancer and make a comparative analysis of the algorithms. The result shows a great advantage of Resnet-34 with an accuracy of $90.40\%$ in classifying cancer images. However, the pretrained VGG-16 demonstrates a higher F1-score because there is less parameters to update. We believe that the field of breast cancer diagnosis stands to benefit greatly from the use of deep transfer learning. Transfer learning may assist to increase the accuracy and accessibility of breast cancer screening by allowing deep learning models to be trained with little data.