MR Images, Brain Lesions, and Deep Learning

Medical brain image analysis is a necessary step in computer-assisted/computer-aided diagnosis (CAD) systems. Advancements both hardware and software the past few years have led to improved segmentation classification of various diseases. In present work, we review published literature on systems algorithms that allow for classification, identification, detection white matter hyperintensities (WMHs) magnetic resonance (MR) images, specifically cases ischemic stroke demyelinating For selection criteria, used bibliometric networks. Of total 140 documents, selected 38 articles deal with main objectives this study. Based discussion revised there constant growth research development new deep learning models achieve highest accuracy reliability lesions. Models good performance metrics (e.g., Dice similarity coefficient, DSC: 0.99) were found; however, little practical application due use small datasets lack reproducibility. Therefore, conclusion should be multidisciplinary groups overcome gap between CAD developments their deployment clinical environment.