Deep Learning Based Computer-Aided Systems for Breast Cancer Imaging : A Critical Review

TL;DR

This review critically examines the past decade's deep learning applications in breast cancer imaging, highlighting advances in computer-aided diagnosis systems that improve accuracy and reduce manual effort.

Contribution

It provides a comprehensive summary of recent deep learning methods in breast tumor diagnosis, emphasizing their effectiveness and potential for future research directions.

Findings

Deep learning methods improve diagnostic accuracy.

DL-CAD systems reduce manual feature extraction.

Deep learning is effective for breast cancer screening.

Abstract

This paper provides a critical review of the literature on deep learning applications in breast tumor diagnosis using ultrasound and mammography images. It also summarizes recent advances in computer-aided diagnosis (CAD) systems, which make use of new deep learning methods to automatically recognize images and improve the accuracy of diagnosis made by radiologists. This review is based upon published literature in the past decade (January 2010 January 2020). The main findings in the classification process reveal that new DL-CAD methods are useful and effective screening tools for breast cancer, thus reducing the need for manual feature extraction. The breast tumor research community can utilize this survey as a basis for their current and future studies.