Enhanced CNN Model
Efficient Brain Stroke Detection with Severity Region Detection
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Artificial Intelligence (AI) is becoming increasingly popular for solving real-world problems in today's era. Combining data science approaches with AI, including machine learning (ML) and deep learning (DL), has become an important interdisciplinary field that addresses solutions across various disciplines. One of the problems being considered in research is the efficient detection of brain strokes in healthcare. According to the World Health Organization (WHO), the incidence of brain strokes is rapidly increasing, leading to severe disabilities and fatalities worldwide. Preventing brain stro...
Artificial Intelligence (AI) is becoming increasingly popular for solving real-world problems in today's era. Combining data science approaches with AI, including machine learning (ML) and deep learning (DL), has become an important interdisciplinary field that addresses solutions across various disciplines. One of the problems being considered in research is the efficient detection of brain strokes in healthcare. According to the World Health Organization (WHO), the incidence of brain strokes is rapidly increasing, leading to severe disabilities and fatalities worldwide. Preventing brain strokes has become a challenging problem for healthcare professionals due to the numerous factors involved. Research into the use of AI for early detection of brain strokes has recently gained significant attention from academia and researchers. From the literature, it is evident that researchers employ both data- driven approaches and imaging technology-based methods. The literature has highlighted three critical problems in AI-enabled approaches for brain stroke detection.
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