Interpretability methods of machine learning algorithms with applications in breast cancer diagnosis. (arXiv:2202.02131v1 [cs.LG])

Early detection of breast cancer is a powerful tool towards decreasing its
socioeconomic burden. Although, artificial intelligence (AI) methods have shown
remarkable results towards this goal, their "black box" nature hinders their
wide adoption in clinical practice. To address the need for AI guided breast
cancer diagnosis, interpretability methods can be utilized. In this study, we
used AI methods, i.e., Random Forests (RF), Neural Networks (NN) and Ensembles
of Neural Networks (ENN), towards this goal and explained and optimized their …

algorithms applications arxiv cancer diagnosis interpretability learning machine machine learning machine learning algorithms