Classification of breast cancer histology images using incremental boosting convolution networks

Abstract

Breast cancer is the most common cancer type diagnosed in women worldwide. While breast cancer can occur in both men and women, it is by far more prevalent in women. Researchers have developed computer-aided systems for efficient diagnosis of breast cancer from histopathological microscopic images. These systems have contributed to increased diagnosis efficiency of biopsy tissue using hematoxylin and eosin stained images. However, most computer-aided diagnosis systems have traditionally used handcrafted feature extraction methods that are both ineffective and time-consuming. In this study, we propose an approach that utilizes deep learning models with convolutional layers to extract the most useful visual features for breast cancer classification. It is shown that these deep learning models can extract better features than handcrafted feature extraction approaches. We also propose a novel boosting strategy to achieve the main goal, whereby the system is efficiently enriched by progressively combining deep learning models (weak classifiers) into a stronger classifier. Our system is used to classify hematoxylin and eosin stained breast biopsy images into two major groups (carcinomas and non-carcinomas) and four classes (normal tissues, benign lesions, in situ carcinomas and invasive carcinomas). We demonstrate applications to breast cancer histopathology images that have been considered challenging to diagnose based on conventional methodologies. Our results demonstrate that our breast cancer classifier with a boosting deep learning model significantly outperforms state-of-the-art methods.