A Stacked Ensemble-Based Classifier for Breast Invasive Ductal Carcinoma Detection on Histopathology Images

Abstract

Breast cancer is one of the main causes of death in women. When body tissues start behaves abnormally and the ratio of tissues growth becomes asymmetrical then this stage is called cancer. Invasive ductal carcinoma (IDC) is the early stage of breast cancer. The early detection and diagnosis of invasive ductal carcinoma is a significant step for the cure of IDC breast cancer. This paper presents a convolutional neural network (CNN) approach to detect and visualize the IDC tissues in breast on histological images dataset. The dataset consists of 90 thousand histopathological images containing two categories: Invasive Ductal Carcinoma positive (IDC+) and Invasive Ductal Carcinoma negative (IDC-). For the detection of this disease, we have proposed an ensemble learning-based deep learning model that consists of the multi-model structure having different internal configuration settings of CNN. As a result, the proposed method has significant progress as per present techniques in terms of accuracy as 92.7%. Therefore, the ensemble approach of multi-model CNN can provide the evolution of IDC at different stages, and this would help the pathologist in the cure of cancer. The experimented results show that the proposed ensemble-based approach of multi-model CNN generated the preeminent results with an accuracy of 92.7% as compared to the already existing techniques.