OmniCortex: A Hybrid Framework for Explainable Multimodal Brain Tumor Classification

The significant research contribution of this study is the development of OmniCortex, a hybrid ResNet50–CNN framework for automated brain tumor classification using multimodal CT and MRI images. The proposed framework introduces a spatial tensor reconstruction layer to preserve spatial information and address cross-modal feature alignment limitations, while a custom CNN refinement head further enhances the high-level features extracted by ResNet50. The model was evaluated on 9,618 CT and MRI images and achieved 98.23% testing accuracy, outperforming the evaluated baseline architectures without requiring artificial data balancing or manual radiomic feature engineering. Furthermore, the integration of Grad-CAM provides visual interpretability by demonstrating that predictions are focused on clinically relevant tumor regions rather than background artifacts, thereby improving the transparency and potential clinical utility of the proposed system.