NeuroTrustNet: a cost-effective multimodal ensemble framework for brain tumor classification under cross-dataset...

Accurate brain tumor classification from Magnetic Resonance Imaging (MRI) remains challenging due to dataset heterogeneity, class imbalance, and limited interpretability, while practical deployment further requires models that balance predictive performance with computational efficiency. In this work, we propose NeuroTrustNet, an integrated multimodal framework that combines complementary convolutional neural network (CNN), Vision Transformer (ViT), and handcrafted radiomic representations through the proposed Adaptive Attention Stacking (AAS) mechanism, enabling sample-specific feature fusion to improve robustness under cross-dataset variability while maintaining deployment-oriented computational efficiency. To evaluate performance under different computational constraints, we consider both high-capacity ensemble models (CNN and ViT ensembles) and lightweight architectures (RapidNet and AdaptoVision) as baseline systems. Experimental results on a large multi-dataset corpus show that while high-capacity ensembles achieve the highest accuracy (up to 96% on an external test set), the proposed NeuroTrustNet maintains competitive performance (94%) while reducing the computational cost of the fusion optimization stage by approximately 80% compared with full end-to-end ensemble training, highlighting its suitability for deployment-oriented medical AI systems operating under computational constraints while maintaining an effective balance between accuracy and efficiency. To further enhance interpretability, we incorporate a post-hoc interpretability component combining visual attribution maps with structured textual summaries derived from model outputs, enabling transparent and human-readable insights without influencing model predictions. The results demonstrate the potential of NeuroTrustNet as a deployment-oriented multimodal decision-support framework, providing a competitive balance between predictive performance, computational efficiency, and interpretability under cross-dataset variability.