Publication

Abstract : Breast cancer (BC) is a potentially life-threatening disease that occurs because of uncontrolled growth of abnormal corpuscles in the breast tissue. Pathologists analyze the tissue structures using histopathological whole slide images to identify cancerous anomalies. However, pathologists face severe challenges such as fatigue, subjectivity, and inter-observer variability in the early detection of BC. Understanding the intricacies of BC from molecular tissue structures is complex, and inexpertise leads to adverse outcomes. This paper proposes a computed aided detection (CAD) system that can assist histopathologists in the early detection of BC, potentially reducing the abnormalities and diagnostic time. Leveraging the power of convolutional neural networks (CNNs), a stacked ensemble-based model is developed to identify benign and malignant cancerous tissues using histopathological images. The ensemble models comprise three deep CNNs, namely MobileNetV2, ShuffleNet, and SqueezeNet, trained on the BreakHis dataset. Finally, individual CNNs predictions are fed to the average voting-based classifier to identify benign and malignant tissues. The stacked ensemble-based deep CNN model outperformed the individual CNN models in BC prediction, achieving superior accuracy and robustness.