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.
@article{arxiv.2010.00961,
title = {Deep Learning Based Computer-Aided Systems for Breast Cancer Imaging : A Critical Review},
author = {Yuliana Jiménez-Gaona and María José Rodríguez-Álvarez and Vasudevan Lakshminarayanan},
journal= {arXiv preprint arXiv:2010.00961},
year = {2020}
}