A Novel Hybrid Deep Learning Approach for Accurate Brain Tumor Detection in MRI Scans

Brain tumors are difficult to diagnose because they have intricate structures and varying imaging scan appearances. Accurate diagnosis and classification at an early stage are important for successful treatment, but current methods have difficulty dealing with segmentation intricacy, few labeled datasets, and data privacy issues. This work suggests a deep learning hybrid strategy combining Convolutional Neural Networks (CNNs) for local feature extraction and Vision Transformers (ViTs) for global spatial pattern capture to improve accuracy and reliability. Federated learning protects data privacy through collaborative training between institutions without sharing patient data. Anisotropic filtering is used for noise removal, and Generative Adversarial Network (GAN)-based augmentation is utilized to handle imbalanced data. Gradient-Weighted Class Activation Mapping (Grad-CAM) improves the explainability of the model with tumor area marking for clinical insight. Tested with real-time MRI data, the model outshines current approaches in segmentation accuracy, classification efficacy, and readability. This work proves the competence of deep learning coupled with privacy-preserving measures for enhanced diagnosis of brain tumors.