Computer-Aided Diagnosis Techniques for Brain Tumor Segmentation and Classification Using MRI

Brain tumors are among the most life-threatening neurological conditions, characterized by the abnormal and uncontrolled growth of brain cells. Early and accurate detection of brain tumors is critical for effective treatment planning and improving patient survival rates. Magnetic Resonance Imaging has emerged as the most prominent non-invasive imaging technique for brain tumor diagnosis. However, manual interpretation of MRI scans is often time-consuming and prone to human error. In response, artificial intelligence (AI)-based methods, particularly those employing machine learning and deep learning, have shown significant potential in enhancing the accuracy and efficiency of tumor segmentation and classification. This paper presents a comprehensive review of various AI-driven techniques, including traditional ML models, deep convolutional neural networks, and hybrid systems, that have been employed for the automatic detection, segmentation, and categorization of brain tumors using MRI data. A detailed comparative analysis of existing methods is provided based on key performance metrics such as accuracy, sensitivity, specificity, Dice score, and area under the curve. Additionally, the paper highlights the challenges associated with model generalization, dataset limitations, preprocessing variability, and computational resource constraints. The review concludes by outlining future research directions aimed at developing more robust, interpretable, and clinically viable CAD systems for brain tumor analysis.