Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: A systematic review. (March 2018)

• Record Type: Journal Article
• Authors: Yassin, Nisreen I.R.; Omran, Shaimaa; El Houby, Enas M.F.; Allam, Hemat
• Is Part Of: Computer methods and programs in biomedicine. Volume 156(2018)
• Page Start: 25
• Page End: 45
• Publication Date: 2018-03
• Subjects: Breast cancer -- Medical image modality -- Classification -- Machine learning techniques -- Computer-aided diagnosis; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2017.12.012
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: This systematic review aims to present the state of the art regarding the computer aided diagnosis (CAD) systems for breast cancer. It provides the current status of CAD systems according to the used image modalities and the machine learning based classifiers. Different analyses have been conducted on the collected data. Abstract: Background and objective: The high incidence of breast cancer in women has increased significantly in the recent years. Physician experience of diagnosing and detecting breast cancer can be assisted by using some computerized features extraction and classification algorithms. This paper presents the conduction and results of a systematic review (SR) that aims to investigate the state of the art regarding the computer aided diagnosis/detection (CAD) systems for breast cancer. Methods: The SR was conducted using a comprehensive selection of scientific databases as reference sources, allowing access to diverse publications in the field. The scientific databases used are Springer Link (SL), Science Direct (SD), IEEE Xplore Digital Library, and PubMed. Inclusion and exclusion criteria were defined and applied to each retrieved work to select those of interest. From 320 studies retrieved, 154 studies were included . However, the scope of this research is limited to scientific and academic works and excludes commercial interests. Results: This survey provides a general analysis of the current status of CAD systems according to the used image modalities and the machine learning based classifiers. Potential research studies have been discussed to create a more objective and efficient CAD systems.