Deep Learning based Advanced Brain Tumor Classification

Detecting brain tumors early and with high accuracy is a difficult challenge in medical imaging. This is because there are many different types of tumors, they often have irregular shapes, and they appear differently in scans. Recent progress in artificial intelligence, particularly deep learning, has opened up new possibilities for automatic and precise tumor detection. This study introduces a full diagnostic system that combines convolutional neural networks (CNNs), Vision Transformers (ViTs), and ensemble methods such as Support Vector Machines (SVMs) and Gradient Boosting Classifiers. To ensure high-quality input data, the system uses advanced pre-processing techniques like removing the skull, normalizing image intensity, and correcting bias fields. When tested on standard MRI datasets, hybrid models- especially those including transformer components- perform better than traditional models in terms of accuracy, sensitivity, and ability to handle various tumor types. To build trust with medical professionals, the system includes Explainable AI features that clarify how the models reach their conclusions. These explanations help doctors better understand and trust the results. Overall, the system shows grat promise in aiding early diagnosis and supporting personalized treatment for brain tumors.