Breast Cancer Diagnosis with Transfer Learning and Global Pooling

TL;DR

This paper presents a deep learning approach using transfer learning and global pooling for accurate breast cancer histology image classification, achieving over 92% accuracy with optimized preprocessing and data augmentation.

Contribution

It introduces a novel combination of transfer learning, stain normalization, and pooling operations for improved breast cancer image classification.

Findings

Achieved 92.50% classification accuracy.

Effective use of stain normalization as preprocessing.

Data augmentation improved model performance.

Abstract

Breast cancer is one of the most common causes of cancer-related death in women worldwide. Early and accurate diagnosis of breast cancer may significantly increase the survival rate of patients. In this study, we aim to develop a fully automatic, deep learning-based, method using descriptor features extracted by Deep Convolutional Neural Network (DCNN) models and pooling operation for the classification of hematoxylin and eosin stain (H&E) histological breast cancer images provided as a part of the International Conference on Image Analysis and Recognition (ICIAR) 2018 Grand Challenge on BreAst Cancer Histology (BACH) Images. Different data augmentation methods are applied to optimize the DCNN performance. We also investigated the efficacy of different stain normalization methods as a pre-processing step. The proposed network architecture using a pre-trained Xception model yields 92.50% average classification accuracy.