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

arXiv: https://arxiv.org/abs/2309.08745 · HTML: https://mlsanity.com/paper/2309.08745/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 49 references that are in the map.
- First posted on arXiv 15 Sep 2023.

## Builds on (most cited first, 8 of 8)

- [BRACS: A Dataset for BReAst Carcinoma Subtyping in H&E Histology Images](https://mlsanity.com/paper/2111.04740/) (2021)
- [BACH: Grand Challenge on Breast Cancer Histology Images](https://mlsanity.com/paper/1808.04277/) (2018)
- [HACT-Net: A Hierarchical Cell-to-Tissue Graph Neural Network for Histopathological Image Classification](https://mlsanity.com/paper/2007.00584/) (2020)
- [Estimate and Replace: A Novel Approach to Integrating Deep Neural Networks with Existing Applications](https://mlsanity.com/paper/1804.09028/) (2018)
- [ScoreNet: Learning Non-Uniform Attention and Augmentation for Transformer-Based Histopathological Image Classification](https://mlsanity.com/paper/2202.07570/) (2022)
- [Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis](https://mlsanity.com/paper/1802.00752/) (2018)
- [Deep learning-based assessment of tumor-associated stroma for diagnosing breast cancer in histopathology images](https://mlsanity.com/paper/1702.05803/) (2017)
- [Convolutional capsule network for classification of breast cancer histology images](https://mlsanity.com/paper/1804.08376/) (2018)

## Abstract (arXiv)

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.