Implementation on “MRI Based Types of Brain Tumor Detection Using CNN”

Brain tumors are critical health conditions resulting from the abnormal and uncontrolled growth of cells within the brain. Timely and precise detection plays a crucial role in improving patient prognosis, but conventional manual diagnosis through Magnetic Resonance Imaging (MRI) scans can be slow and susceptible to human errors. To overcome these limitations, we present a sophisticated deep learning framework designed to enhance the classification and detection of brain tumors using a hybrid approach that combines EfficientNetV2 with Vision Transformer (ViT) models. Our approach incorporates advanced image enhancement through super- resolution techniques, adaptive morphological preprocessing, and GAN-based data augmentation to enrich image quality and dataset diversity. Moving beyond traditional 2D slice analysis, we leverage 3D Convolutional Neural Networks (3D CNNs) and U-Net architectures to fully exploit volumetric MRI data for more accurate tumor localization. To optimize training, we employ Focal Loss, the Lookahead AdamW optimizer, and a Cyclical Learning Rate (CLR) schedule, boosting both convergence speed and model robustness. Furthermore, we incorporate explainable AI (XAI) methods such as Grad-CAM, SHAP, and Bayesian Deep Learning to enhance model transparency and quantify predictive uncertainty. Extensive experimentation on standard MRI datasets reveals that our hybrid model achieves an outstanding classification accuracy of 99.12%, outperforming established CNN architectures like VGG16, InceptionV3, Xception, and ResNet50. The system is engineered for clinical practicality, supporting lightweight deployment through TFLite and ONNX, and enabling federated learning for secure, privacy-preserving model training across healthcare facilities. These results confirm that our proposed solution significantly advances tumor detection accuracy, interpretability, and operational efficiency, making it highly suitable for real-world clinical applications.