Multi-path Convolutional Neural Network to Identify Tumorous Sub-classes for Breast Tissue from Histopathological Images

Abstract

Malignancy is one of the leading causes of death. It is on the rise in the developed and low-income countries with survival rates of less than 40%. However, early diagnosis may increase survival chances. Histopathology images acquired from the biopsy are a popular method for cancer diagnosis. In this work, we propose a deep convolutional neural network-based method that helps classify breast cancer tumor subtypes from histopathology images. The model is trained on the BreakHis dataset but is also tested on images from other datasets. The model is trained to recognized eight different tumor subtypes, and also to perform binary classification (malignant/non-malignant). The CNN model combines an encoder–decoder architecture and a parallel feed-forward network with attention mechanism. The proposed model provides state-of-the-art scores. Comparing with the other models, the accuracy of the proposed model is higher at different magnification and patient levels. The implementation is available at github.com/rangan2510/Residual\(\_\)Unet