Brain Stroke Detection using CT Images

It is through stroke that disability and mortality are caused in most populations worldwide; therefore, fast detection and accuracy for timely intervention are required. This project is developing an advanced brain stroke detection system based on a combination of medical imaging and machine learning algorithms. It uses data from the CT scan and applies image processing to extract features such as ischemic areas, hemorrhagic regions, and perfusion deficits. The project is designed to train machine learning models, specifically deep learning architectures such as CNNs, for automatic classification of stroke types and severity, including ischemic and hemorrhagic, with a view to detecting sensitivity and accuracy as well as specificity and specificity, which will be used to validate the reliability of the model. This project directly points to a more efficient diagnosis of strokes by healthcare professionals, less delay in commencing treatment, and improved patient outcomes. Its solution allows integration into hospital systems for the better diagnostic capabilities in cities and outside but without neurologists’ access.