Improved Breast Cancer Diagnosis through Transfer Learning on Hematoxylin and Eosin Stained Histology Images

TL;DR

This study enhances breast cancer diagnosis accuracy using transfer learning on histology images, achieving up to 96.2% F1-score with optimized deep learning models and data strategies.

Contribution

It introduces a transfer learning approach with data augmentation and upsampling to improve classification of breast cancer subtypes in histology images.

Findings

ResNet50 achieved 66% F1-score with default split.

Upsampling and augmentation increased F1-score to 96.2%.

False positive and false negative rates were reduced below 3%.

Abstract

Breast cancer is one of the leading causes of death for women worldwide. Early screening is essential for early identification, but the chance of survival declines as the cancer progresses into advanced stages. For this study, the most recent BRACS dataset of histological (H\&E) stained images was used to classify breast cancer tumours, which contains both the whole-slide images (WSI) and region-of-interest (ROI) images, however, for our study we have considered ROI images. We have experimented using different pre-trained deep learning models, such as Xception, EfficientNet, ResNet50, and InceptionResNet, pre-trained on the ImageNet weights. We pre-processed the BRACS ROI along with image augmentation, upsampling, and dataset split strategies. For the default dataset split, the best results were obtained by ResNet50 achieving 66% f1-score. For the custom dataset split, the best results were obtained by performing upsampling and image augmentation which results in 96.2% f1-score. Our second approach also reduced the number of false positive and false negative classifications to less than 3% for each class. We believe that our study significantly impacts the early diagnosis and identification of breast cancer tumors and their subtypes, especially atypical and malignant tumors, thus improving patient outcomes and reducing patient mortality rates. Overall, this study has primarily focused on identifying seven (7) breast cancer tumor subtypes, and we believe that the experimental models can be fine-tuned further to generalize over previous breast cancer histology datasets as well.