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/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.
Highlights
Due to the anatomy of the human body, women are more vulnerable to breast cancer than men.Breast cancer is one of the leading causes of death for women globally [1,2,3,4] and is a significant public health problem
The search was designed to identify all studies in which Digital mammography (DM) and US were evaluated as a primary detection modality for breast cancer, and were both used for screening and diagnosis
The results showed that the deep learning (DL)-computer-aided diagnosis/detection (CAD) system is able to detect
Summary
Due to the anatomy of the human body, women are more vulnerable to breast cancer than men. Breast cancer is one of the leading causes of death for women globally [1,2,3,4] and is a significant public health problem. It occurs due to the uncontrolled growth of breast cells. These cells usually form tumors that can be seen from the breast area via different imaging modalities. Some basic knowledge about the normal structure of the breast is important. Women’s breasts are constructed of lobules, ducts, nipples, and fatty tissues (Figure 1) [5]
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