  Medical brain image analysis is a necessary step in Computer Assisted /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 MRI images specifically in cases
of ischemic stroke and demyelinating diseases. For the selection criteria, we
used the bibliometric networks. Out of a total of 140 documents we selected 38
articles that deal with the main objectives of this study. Based on the
analysis and discussion of the revised documents, there is constant growth in
the research and proposal of new models of deep learning to achieve the highest
accuracy and reliability of the segmentation of ischemic and demyelinating
lesions. Models with indicators (Dice Score, DSC: 0.99) were found, however
with little practical application due to the uses of small datasets and lack of
reproducibility. Therefore, the main conclusion is to establish
multidisciplinary research groups to overcome the gap between CAD developments
and their complete utilization in the clinical environment.
