Hybrid Deep Learning and Radiomic Shape Feature Fusion for Automated Brain Tumor Classification from MRI Images

Brain tumor diagnosis using magnetic resonance imaging is a critical challenging task due to variations in tumor appearance, class imbalance, and limited labeled medical data. Accurate and robust automated classification systems can significantly assist radiologists in early diagnosis and clinical decisionmaking. We propose the hybrid EfficientNet, Restnet1 based deep learning frameworks for both binary (tumor vs. no tumor) and multiclasses brain tumor classification using MRI images. The transfer learning with EfficientNetB0 as the backbone for effective feature extraction, combined with global average pooling and global max pooling to capture complementary spatial information. To enhance robustness and discriminative capability, an adaptive feature fusion strategy used, integrating deep CNN features with auxiliary radiomic features. Separate preprocessing pipelines and augmentation strategies are designed for binary and multiclass datasets to address data heterogeneity and class imbalance. The models are trained using a two-phase strategy involving frozen-backbone training followed by controlled finetuning. Extensive experiments are conducted on two publicly available brain MRI datasets. The proposed framework achieves 94% accuracy in binary classification and$\mathbf{8 4. 5 \%}$accuracy in multiclass classification, with high tumor recall and balanced performance across classes. Experimental results and ablation studies demonstrate that hybrid feature fusion significantly improves classification performance compared to baseline CNN models. The proposed approach offers a robust, efficient, and clinically applicable solution for automated brain tumor classification in MRI.