Machine Learning and Image Processing for Breast Cancer: A Systematic Map

Machine Learning (ML) combined with Image Processing (IP) gives a powerful tool to help physician, doctors and radiologist to make more accurate decisions. Breast cancer (BC) is a largely common disease among women worldwide; it is one of the medical sub-field that are experiencing an emergence of the use of ML and IP techniques. This paper explores the use of ML and IP techniques for BC in the form of a systematic mapping study. 530 papers published between 2000 and August 2019 were selected and analyzed according to 6 criteria: year and publication channel, empirical type, research type, medical task, machine learning objectives and datasets used. The results show that classification was the most used ML objective. As for the datasets most of the articles used private datasets belonging to hospitals, although papers using public data choose MIAS (Mammographic Image Analysis Society) which make it as the most used public dataset. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.