Machine Learning for Breast Cancer Detection: A Systematic Literature Review - Yessenia EGUSQUIZA-LÓPEZ; Javier GAMBOA-CRUZADO; Fernando CELI-GARCÍA; José COVEÑAS-LALUPU; María CAMPOS-MIRANDA; Magno ATUJE-PARIONA (Communications of International Proceedings, 2021)
The advancement in machine learning presents a plethora of techniques for breast cancer detection, thus deviating from the traditional mammographic image verification procedures. A Systematic Literature Review (SLR) of using machine learning for breast cancer detection was conducted from 2015 to 2020. The search strategy identified 3 145 058 articles from digital libraries, such as Google Scholar, Springer, ACM Digital Library, IEEE Xplore, ProQuest, Taylor & Francis Online, IOPscience, Microsoft Academic, Web of Science, and ARDI, and only 83 articles were considered based on the exclusion criteria. The results of the systematic review have focused on recent machine learning studies that offer better techniques for an efficient breast cancer detection; it also provides a mapping of the extracted studies to shop for relevance to their settings and situations.
Keywords: Systematic Literature Review; Machine Learning; Mammograms; Breast Cancer Detection