ARCHIVES

Brain tumor detection and classification is a vital task in medical imaging, as early and accurate diagnosis can significantly improve treatment outcomes and patient survival rates. Manual evaluation of MRI scans by radiologists is often time-consuming, subjective, and prone to errors, particularly in high-pressure environments or areas lacking medical expertise. To address these challenges, this project proposes an automated brain tumor detection and classification system using Deep Learning, specifically Convolutional Neural Networks (CNNs). The system utilizes publicly available MRI datasets containing glioma, meningioma, pituitary tumors, and normal brain scans. Preprocessing techniques such as resizing, normalization, and augmentation are applied to enhance data quality and improve model generalization. The trained CNN is then integrated into a Flask-based web application that allows real-time tumor detection and classification from uploaded MRI scans. Performance evaluation is carried out using accuracy, precision, recall, and F1-score to validate the system’s robustness. The proposed method provides an efficient, scalable, and user-friendly solution for supporting medical professionals in diagnostic decision-making, reducing delays, and minimizing misclassification errors. This approach demonstrates the potential of AI-powered tools to enhance healthcare delivery, especially in remote and resource-limited settings.