Classification and Diagnosis of Invasive Ductal Carcinoma Using Deep Learning

Abstract

In the past decades, researchers have demonstrated abilities to automate the process of detection and analysis of different kinds of cancers using Whole Slide Images (WSI) datasets. The breast cancer detection in histopathology images (one of the WSI dataset) using deep learning is one of the key research areas among the Computer AiDed (CAD) diagnostic systems. When it is done manually, it is a very tedious and challenging task for a pathologist as it involves thorough scanning of tissues to detect malignancy. This paper presents Convolutional Neural Network (CNN) classifier for breast cancer detection on the Breast Histopathology Images (BHI) dataset. A confusion matrix is computed for the BHI samples to analyze the prediction results of the CNN classifier. The CNN detects carcinoma tissues while labeling 55,505 image test samples as positive or negative; and achieves accuracy of 84.93%, recall of 84.70% and F-measure as 76.07% respectively.