Abstract
AbstractAlthough Breast Cancer (BC) deaths have decreased over time, it is still the second largest cause of cancer death among women. With the technical revolution of Artificial Intelligence (AI), and the big healthcare data that is becoming more of a reality, many researchers have attempted to employ Machine Learning (ML) techniques to gain a better understanding of this disease. The present paper is a systematic mapping study of the application of ML techniques in Breast Cancer Screening (BCS) between the years 2011 and early 2021. Out of 129 candidate papers we retrieved from six digital libraries, a total of 66 papers were selected according to 5 criteria: year and publication venue, paper type, BCS modality, and empirical type. The results show that classification was the most used ML objective, and that mammography was the most frequent BCS modality used.KeywordsMachine learningBreast cancerScreeningSystematic mapping study
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