Design Guidelines for Mammogram-Based Computer-Aided Systems Using Deep Learning Techniques

Abstract

Breast cancer is the second fatal disease among cancers patients both in Canada and across the globe. However, when detected early, a patients' survival rate can be raised. Thus, researchers and scientists have been practicing to develop Computer-Aided Detection (CADe) and Computer-Aided Diagnosis (CADx) systems. Traditional CAD systems depend on manual feature extraction, which has provided radiologists with poor detection and diagnosis tools. Nevertheless, recently, the powerful application of Convolutional Neural Networks (CNN)s as one of the deep learning-based methods has revolutionized these systems' accuracy and development. This article proposes categorizing the current deep learning research on mammogram types based on researchers' techniques for their empirical studies. Also, we provide an overview of different publicly available data resources and available datasets for breast imaging. This critical review of the state-of-the-art techniques is presented, which we believe can serve as a valuable source for research scientists investigating deep learning-based breast mammogram classification.