Deep Learning-Based Classification of Brain Tumors from Magnetic Resonance Imaging Scans Using a Convolutional Neural Network Model
Brain tumors are serious neurological conditions that require accurate and timely classification to support medical evaluation and treatment planning. This project presents a deep learning-based system for classifying brain Magnetic Resonance Imaging (MRI) scans into four categories: glioma, meningioma, pituitary tumor, and no tumor. The proposed system uses a Convolutional Neural Network (CNN) trained on a balanced dataset of 7,200 MRI images collected from publicly available sources. The images were preprocessed through RGB conversion, resizing, tensor transformation, and normalization to ensure consistent input for model training and testing. The trained model achieved an overall classification accuracy of 94.31% on a held-out test set of 1,600 MRI images, demonstrating strong performance in multi-class brain tumor classification. A Streamlit-based web application was also developed to allow users to upload MRI images and view the predicted class, confidence score, and probability distribution across the four categories. The system is intended for educational and research purposes only and should not replace professional medical diagnosis, clinical judgment, or radiological evaluation.
Volume & Issue
Vol. Volume 6 / Iss. Issue 1