Convolutional Neural Network Based Tumor Classification Using Brain MRI Images

Classifying brain tumors is crucial for accurate diagnosis and treatment planning in medical imaging. In hospitals, research facilities, and AI-powered diagnostic tools, tumor classification aids in early detection, personalized treatment planning, and monitoring disease progression to improve patient care and outcomes. This study employs a convolutional neural network (CNN), specifically designed for medical imaging tasks, to classify brain MRI scans into four categories: pituitary, meningioma, glioma, and no tumor. The model achieves a validation accuracy of 95.27% after 50 epochs, with performance further assessed through the confusion matrix, precision, recall, and F1-score. For a more thorough assessment, future analyses will incorporate PR curves, ROC-AUC, and performance variance over several runs. This scalable approach not only enhances diagnostic speed but also supports more targeted treatments, ultimately leading to better patient outcomes and more efficient healthcare delivery.