BRAIN TUMOR DETECTION AND CLASSIFICATION

Computer Engineering

This report presents the findings and outcomes of the mini project titled "Brain Tumo Detection And Classification," conducted as part of the Mini Project under the Department of Information Science & Engineering, Visvesvaraya Technological Technology. Early detection is crucial for effective brain tumor treatment, given the significant risks to patients' lives. The study proposes a distinctive method for automatically identifying and categorizing brain cancers using medical imaging data, specifically magnetic resonance imaging (MRI) scans. The technology facilitates precise, efficient, and real-time diagnostics by employing advanced image processing and machine learning algorithms to analyse brain images. A primary research objective is the development of an automated method for detecting brain cancers in MRI scans. Subsequently, a machine learning model is trained on a large and diverse dataset to classify tumors into various groups, including pituitary, glioma, and meningioma. The system's performance is rigorously tested using a substantial and varied dataset to ensure its therapeutic relevance. The overarching research goal is to achieve high sensitivity, specificity, and accuracy in tumor detection while minimizing false positives and negatives. The proposed system has the potential to assist medical practitioners in diagnosing patients more rapidly and accurately, thereby enhancing patient outcomes. This research and development project underscores the potential of combining medical imaging and artificial intelligence for early detection and classification of brain tumors, offering the prospect of reduced diagnostic time and increased effectiveness in healthcare systems.

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