Article
A DEEP LEARNING-BASED DIAGNOSTIC MODEL USING NEUROIMAGES (BRAIN-STROKE DIAGNOSIS)
Stroke is one of the most prevalent causes of death and disability in the world, although it is preventable and treated. Improving clinical outcomes and lowering the burden of disease are significantly aided by early stroke detection and prompt treatments. Because machine learning techniques can be used to detect strokes, they have garnered a lot of attention in recent years. Finding trustworthy techniques, algorithms, and characteristics that support healthcare providers in making well-informed decisions on stroke prevention and treatment is the goal of this project. In order to accomplish this, we have created an early stroke detection system that uses CT scans of the brain and a ResNet (Residual Network) model to identify strokes at an extremely early stage. The ResNet model is used for image classification in order to obtain the most pertinent features for categorization. The system's efficacy was assessed using cross-validation using metrics including precision, recall, F1 score, ROC (Receiver Operating Characteristic Curve), and AUC (Area Under the Curve). The suggested diagnostic system enables doctors to treat stroke patients with knowledge.
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