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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 make more accurate decisions. Breast cancer which 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 machine learning and image processing techniques for breast cancer 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 techniques, datasets used, validation methods and performance measures. The results shows that diagnosis was the most used medical task and that Neural Networks were largely used to perform classification in the selected papers. As for the datasets and considering the privacy of images, most of the articles used private datasets belonging to hospitals, although papers using public data chose MIAS database that make it as the most used public dataset.