Abstract
A stroke, a fatal brain disorder with systemic consequences, emphasizes the crucial need of timely treatment. Recent studies emphasize the "time is brain" notion, which states that starting therapy within six hours improves outcomes. Manual stroke diagnosis by neuroradiologists is commonplace, but subjective and time-consuming. This study examines and views methods for classifying stroke lesions, with an emphasis on Machine Learning and Deep Learning for brain scan processing. Deep Learning thrives on complicated data but necessitates many resources. Simpler architecture is desired. The study's goal is to improve stroke classification, allowing for faster, more precise medical choices and treatment. This research has the potential to lead to enhanced healthcare solutions powered by intelligent systems.
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