A systematic survey of deep learning in breast cancer. Issue 1 (20th August 2021)

• Record Type: Journal Article
• Authors: Yu, Xiang; Zhou, Qinghua; Wang, Shuihua; Zhang, Yu‐Dong
• Is Part Of: International journal of intelligent systems. Volume 37:Issue 1(2022)
• Page Start: 152
• Page End: 216
• Publication Date: 2021-08-20
• Subjects: breast cancer -- CAD systems -- deep learning -- systematic review; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; 006.3
• DOI: 10.1002/int.22622
• Languages: English
• ISSNs: 0884-8173

Abstract

Abstract: In recent years, we witnessed a speeding development of deep learning in computer vision fields like categorization, detection, and semantic segmentation. Within several years after the emergence of AlexNet, the performance of deep neural networks has already surpassed human being experts in certain areas and showed great potential in applications such as medical image analysis. The development of automated breast cancer detection systems that integrate deep learning has received wide attention from the community. Breast cancer, a major killer of females that results in millions of deaths, can be controlled even be cured given that it is detected at an early stage with sophisticated systems. In this paper, we reviewed breast cancer diagnosis, detection, and segmentation computer‐aided (CAD) systems based on state‐of‐the‐art deep convolutional neural networks. The available data sets also indirectly determine CAD systems' performance, so we introduced and discussed the details of public data sets. The challenges remaining in CAD systems for breast cancer are discussed at the end of this paper. The highlights of this survey mainly come from three following aspects. First, we covered a wide range of the basics of breast cancer from imaging modalities to popular databases in the community; Second, we presented the key elements in deep learning to form the compactness for methods mentioned in reviewed papers; Third and lastly, the summative details in each reviewed paper are provided so that interested readers can have a refined version of these works without referring to original papers. Therefore, this systematic survey suits readers with varied backgrounds and will be beneficial to them.