Classification of breast cancer histology images using transfer learning

TL;DR

This paper presents a transfer learning approach using CNNs to classify breast cancer histology images into four categories, achieving high accuracy and demonstrating the effectiveness of fine-tuning pre-trained models for medical image analysis.

Contribution

The study introduces a transfer learning method with CNNs for breast histology classification, outperforming previous approaches and demonstrating the potential of pre-trained models in medical diagnostics.

Findings

ResNet50 achieved 97.50% accuracy

Inception-V3 achieved 91.25% accuracy

Color normalization improved classification performance

Abstract

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, \textit{in 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%.