Automated Brain Tumour Detection Using MRI Scans

Abstract

Brain tumors are among the most critical neurological conditions, requiring timely diagnosis to improve treatment effectiveness and patient survival rates. Magnetic Resonance Imaging (MRI) is one of the most reliable imaging techniques for detecting brain abnormalities because of its superior image quality and detailed visualization of soft tissues. This research proposes an automated brain tumor detection framework that combines image processing and machine learning techniques to identify tumors from MRI scans with high precision. The proposed system follows a structured workflow that includes image enhancement, preprocessing, segmentation, feature extraction, and classification. These stages help distinguish healthy brain tissues from tumor-affected regions efficiently. Deep learning models, particularly Convolutional Neural Networks (CNNs), are utilized to learn complex image patterns and improve diagnostic accuracy while minimizing manual intervention. The model is trained and tested using publicly available MRI datasets to ensure reliability and robustness. Performance evaluation demonstrates strong results in terms of accuracy, sensitivity, specificity, and overall classification efficiency. Experimental findings indicate that the framework can successfully detect and localize brain tumors, providing valuable support to radiologists during the diagnostic process. By enabling faster and more consistent tumor identification, the proposed system has the potential to enhance clinical decision-making, reduce diagnostic errors, and facilitate early treatment planning. This intelligent approach contributes to the advancement of computer-aided diagnosis systems and supports improved healthcare outcomes for patients affected by brain tumors.