applied sciences Review MR Images, Brain Lesions, and Deep Learning Darwin Castillo 1,2,3,* , Vasudevan Lakshminarayanan 2,4 and María José Rodríguez-Álvarez 3 1 Departamento de Química y Ciencias Exactas, Sección Fisicoquímica y Matemáticas, Universidad Técnica Particular de Loja, San Cayetano Alto s/n, Loja 11-01-608, Ecuador 2 Theoretical and Experimental Epistemology Lab, School of Optometry and Vision Science, University of Waterloo, Waterloo, ON N2L3G1, Canada;
[email protected] 3 Instituto de Instrumentación para Imagen Molecular (i3M) Universitat Politècnica de València—Consejo Superior de Investigaciones Científicas (CSIC), E-46022 Valencia, Spain;
[email protected] 4 Departments of Physics, Electrical and Computer Engineering and Systems Design Engineering, University of Waterloo, Waterloo, ON N2L3G1, Canada * Correspondence:
[email protected]; Tel.: +593-07-370-1444 (ext. 3204) Featured Application: This review provides a critical review of deep/machine learning algo- rithms used in the identification of ischemic stroke and demyelinating brain diseases. It eval- uates their strengths and weaknesses when applied to real world clinical data. Abstract: Medical brain image analysis is a necessary step in computer-assisted/computer-aided diagnosis (CAD) systems. Advancements in both hardware and software in the past few years have led to improved segmentation and classification of various diseases. In the present work, we review the published literature on systems and algorithms that allow for classification, identification, and detection of white matter hyperintensities (WMHs) of brain magnetic resonance (MR) images, specifically in cases of ischemic stroke and demyelinating diseases. For the selection criteria, we used bibliometric networks. Of a total of 140 documents, we selected 38 articles that deal with the main objectives of this study.