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Artificial intelligence in cerebral stroke images classification and segmentation: A comprehensive study

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Abstract

A brain stroke, commonly called as a cerebral vascular accident (CVA) is one of the deadliest diseases across the globe and may lead to various physical impairments or even death. Therefore, timely detection, diagnosis, and treatment of said medical emergency are urgent requirements to minimize life loss, which is not affordable in any sense. Nowadays, with the advancements in Artificial Intelligence, Machine Learning, and Deep Learning, these techniques are popularly used in the domain of medical image analysis in general, and brain stroke imaging in particular, to automate image analysis more accurately and less human error-prone. The aim of this state-of-the-art study is to explore the latest computer-aided diagnosis (CAD) techniques to detect, classify, and segment cerebral strokes. Available methods or techniques, and tools used for analysis have been presented and compared with their attained results. In this study, available open-access datasets in the domain of brain stroke analysis have been explored and presented. Moreover, the research also includes the major challenges and provides researchers with applicable future directions.

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  1. National Institute of Technology, Kurukshetra, India

    Gourav Kumar Sharma, Santosh Kumar & Mahendra Kumar Murmu

  2. Delhi Technological University, Delhi, India

    Virender Ranga

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Sharma, G.K., Kumar, S., Ranga, V. et al. Artificial intelligence in cerebral stroke images classification and segmentation: A comprehensive study. Multimed Tools Appl 83, 43539–43575 (2024). https://doi.org/10.1007/s11042-023-17324-3

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