A ResNet50-Based Deep Learning Framework with Optimized MRI Preprocessing

This study introduces an advanced framework for the automated detection and classification of brain tumors, utilizing a ResNet50-based deep learning model integrated with advanced image preprocessing techniques. The model uses the Brain Tumor MRI Dataset (7,023 images from Figshare, SARTAJ, and Br35H) to sort MRI scans into four groups: glioma, meningioma, no tumor, and pituitary. A strong preprocessing pipeline improves image quality by dealing with differences in MRI protocols. The ResNet50 architecture, which uses transfer learning and a custom classification head, is very accurate and easy to understand, which is important for clinical use. Accuracy, precision, recall, and F1-score are all parts of a full evaluation. The model surpasses baseline CNN methodologies, providing enhanced diagnostic accuracy. This study pushes neuro-oncology forward by offering a scalable, understandable way to find tumors early, which could lower the number of mistakes made in diagnosis and improve patient outcomes. Future tasks involve multi-modal integration and refinement to enhance performance further.