Advanced Brain Tumor Classification Sytem Using Deeplearning

Diagnosing brain tumors early and accurately is a tough task in medical imaging. The challenge comes from many tumor types, their irregular shapes, and differing appearance patterns in scans. Recent advances in artificial intelligence, especially deep learning, open new doors for automatic and accurate tumor detection. This study presents a complete diagnostic system that combines convolutional neural networks (CNNs), Vision Transformers (ViTs), and ensemble techniques like Support Vector Machines (SVM) and Gradient Boosting Classifiers. To get the best input data, the system uses advanced preprocessing steps such as removing the skull, normalizing the image intensity, and correcting bias fields. Testing on common MRI datasets shows that hybrid models, especially those with transformer modules, outperform traditional models in accuracy, sensitivity, and ability to handle different tumor types. To build trust with healthcare professionals, the system includes Explainable AI features that explain how the models make decisions. These insights make it easier for doctors to understand and trust the results.