Journal Article

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

Rodríguez-Álvarez, María José, Lakshminarayanan, Vasudevan, Jiménez-Gaona, Yuliana

2020

Overview

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/detection (CAD) systems, which make use of new deep learning methods to automatically recognize breast images and improve the accuracy of diagnoses made by radiologists. This review is based upon published literature in the past decade (January 2010–January 2020), where we obtained around 250 research articles, and after an eligibility process, 59 articles were presented in more detail. The main findings in the classification process revealed 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.

• Publisher: MDPI AG
• Subject: Breast cancer / computer-aided diagnosis / Computer-aided medical diagnosis / convolutional neural networks / Datasets / deep learning / Diagnosis / Diagnostic imaging / Machine learning / mammography / Methods / Neural networks / Online data bases / Technology application / ultrasound