Abstract
One of the main causes of death and permanent disability, stroke needs to be diagnosed as soon as possible, to guarantee early treatment as soon as possible. Traditional stroke detection techniques mostly depend on radiologists who manually interpret neuroimage, which can be laborious and subjective. Many deep learning models lack integration with a user-central design or deployed clinical equipment, despite the promise to detect strokes despite recent advances. The study introduces a novel AI-powered structure to an early stroke classification from CT and MRI neuroimage that uses a modified VGG16-based Convolutional Neural Network (CNN). Our technology, unlike traditional models, includes a dual-interface web platform, designed for patients and physicians, which is filled with AI-Interested Chatbot, automated report distribution and a safe database. The suggested model was a high precision and remembered and a classification accuracy of 94.6%. These findings support the efficacy of the framework and demonstrate their ability for practical clinical integration, especially in rural or deprived healthcare settings.
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