# Classification of breast cancer histology images using transfer learning

arXiv: https://arxiv.org/abs/1802.09424 · HTML: https://mlsanity.com/paper/1802.09424/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2018.

## Built on by (most cited first, 5 of 5)

- [Comparison of fine-tuning strategies for transfer learning in medical image classification](https://mlsanity.com/paper/2406.10050/) (2024)
- [Magnification Prior: A Self-Supervised Method for Learning Representations on Breast Cancer Histopathological Images](https://mlsanity.com/paper/2203.07707/) (2022)
- [Augmenting Medical Imaging: A Comprehensive Catalogue of 65 Techniques for Enhanced Data Analysis](https://mlsanity.com/paper/2303.01178/) (2023)
- [A lightweight deep learning pipeline with DRDA-Net and MobileNet for breast cancer classification](https://mlsanity.com/paper/2403.11135/) (2024)
- [Interpretable Tile-Based Classification of Paclitaxel Exposure](https://mlsanity.com/paper/2510.23363/) (2025)

## Abstract (arXiv)

Breast cancer is one of the leading causes of mortality in women. Early detection and treatment are imperative for improving survival rates, which have steadily increased in recent years as a result of more sophisticated computer-aided-diagnosis (CAD) systems. A critical component of breast cancer diagnosis relies on histopathology, a laborious and highly subjective process. Consequently, CAD systems are essential to reduce inter-rater variability and supplement the analyses conducted by specialists. In this paper, a transfer-learning based approach is proposed, for the task of breast histology image classification into four tissue sub-types, namely, normal, benign, textitin situ carcinoma and invasive carcinoma. The histology images, provided as part of the BACH 2018 grand challenge, were first normalized to correct for color variations resulting from inconsistencies during slide preparation. Subsequently, image patches were extracted and used to fine-tune Google`s Inception-V3 and ResNet50 convolutional neural networks (CNNs), both pre-trained on the ImageNet database, enabling them to learn domain-specific features, necessary to classify the histology images. The ResNet50 network (based on residual learning) achieved a test classification accuracy of 97.50% for four classes, outperforming the Inception-V3 network which achieved an accuracy of 91.25%.