FUSING IMAGE AND CLINICAL DATA FOR PRECISE BRAIN DISEASE DIAGNOSIS USING MACHINE LEARNING

Computer Engineering

The diagnosis of brain diseases poses a significant challenge due to the complexity and heterogeneity of these conditions. Leveraging machine learning (ML) techniques, particularly through the fusion of medical images and clinical data, holds promise for improving diagnostic accuracy and facilitating precise treatment decisions. In this project, we propose a novel approach that integrates both modalities to enhance brain disease diagnosis. We collect a comprehensive dataset comprising medical images (such as MRI or CT scans) and associated clinical data, including demographics, symptoms, and medical history, from patients with various brain diseases. Through careful preprocessing and feature extraction, we capture the rich information embedded in both types of data. We develop a machine learning model capable of effectively integrating these heterogeneous data sources, employing techniques such as multi-modal learning or end-to-end architectures. The model is trained and evaluated using appropriate performance metrics to assess its diagnostic accuracy and clinical utility. Upon validation, the model is deployed into clinical workflows, providing clinicians with a tool for more precise and informed decision-making. Continuous monitoring and improvement ensure the model's reliability and relevance in real-world clinical settings. By bridging the gap between medical images and clinical data, our approach contributes to advancing personalized medicine and improving patient outcomes in the diagnosis and management of brain diseases.

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