Application of Machine Learning in Identifying Brain Strokes

Brain stroke, a critical medical condition, requires prompt diagnosis and treatment to minimize its adverse effects. Machine learning techniques have shown promise in aiding the early detection of brain stroke through medical imaging analysis. This study proposes the utilization of two advanced deep learning architectures, namely ResNet and U-Net, for the automated detection and segmentation of brain stroke. ResU-Net, an enhanced version of U-Net used incorporates residual connections to improve information flow and model performance. Leveraging a dataset comprising medical images of stroke-afflicted brains, the proposed models are trained to accurately segment and classify regions indicative of stroke presence. Metrics including accuracy, sensitivity, specificity, and Dice coefficient are used to evaluate the models ability to detect the brain damage. The ability of all three layouts in precisely identifying brain stroke locations is demonstrated by experimental data. The U-Net exhibits slightly superior performance over ResNet. Performance of ResU-Net in the proposed approach holds promise for assisting healthcare professionals in early diagnosis and further refinement and validation of these models on larger and diverse datasets enhance their generalizability and clinical utility in real-world settings. Real-time image capture and classification, along with immediate feedback, contribute to its mission of advancing stroke diagnosis and potentially saving lives through AI-driven healthcare improvement.