mlsanityData to 7 Oct 2026

MRI Images, Brain Lesions and Deep Learning

arXiv 2101.05091 ↗First posted 13 Jan 2021eess.IV cs.CV cs.LGcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 34 of its 241 references that are in the map. First posted on arXiv 13 Jan 2021.

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34 of 241 references.

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22 more
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