Recent Advances in Artificial Intelligence and Hybrid Model for Enhanced Brain Tumor Classification Using MRI

This study presents a comprehensive evaluation of state-of-the-art artificial intelligence (AI) techniques for brain tumor classification using magnetic resonance imaging (MRI). Leveraging publicly available datasets comprising over 7,000 T1-weighted contrast-enhanced scans across glioma, meningioma, pituitary tumor, and normal classes, we implemented rigorous preprocessing, including noise reduction, skull stripping, normalization, and contrast enhancement. Multiple deep learning architectures, including VGG, ResNet, DenseNet, EfficientNet, and custom convolutional neural networks (CNNs), were trained using transfer learning and fine-tuning strategies. Hybrid and ensemble models combining CNNs with Long Short-Term Memory (LSTM) networks and optimization algorithms further improved diagnostic accuracy. Our best-performing model, EfficientNetB4 with Adam optimization and targeted data augmentation, achieved a classification accuracy of 99.66% and a perfect F1-score, demonstrating clinical-grade performance. Explainable AI techniques such as Grad-CAM, LIME, and SHAP were employed to enhance model interpretability and foster clinical trust. The study highlights the potential of AI-driven diagnostics to surpass traditional methods by offering rapid, objective, and highly accurate tumor classification, facilitating personalized treatment planning. Challenges related to dataset heterogeneity, generalizability, and ethical deployment are discussed, along with future directions involving multimodal data integration and federated learning. These advancements underscore AI’s transformative role in neuro-oncology diagnostics.