Explainable AI for Brain Tumor Classification Using Cross-Gated Multi-Path Attention Fusion and Gate-Consistency Loss

Accurate and early classification of brain tumors using magnetic resonance imaging (MRI) plays a vital role in guiding timely diagnosis and therapeutic intervention. Traditional convolutional neural networks (CNNs) excel at capturing local anatomical patterns but often struggle with contextual reasoning, while Vision Transformers (ViTs) model long-range dependencies but may overlook fine-grained tumor details. To bridge this gap, we propose a hybrid deep learning framework that fuses ResNet and ViT backbones through a Cross-Gated Multi-Path Attention Fusion (CG-MAF) mechanism. CG-MAF performs dynamic, pixel-wise integration of local and global features using cross-attention and learnable gating, thereby adapting to the heterogeneous texture and spatial complexity of brain tumors. To further enhance interpretability and predictive stability, a gate-consistency loss is introduced, which enforces alignment between dual-branch attentional cues. Evaluated on a composite four-class brain MRI dataset, our model achieves state-of-the-art accuracy of 99.81% and an AUC of 0.9999. Ablation studies confirm the critical role of CG-MAF and gate-consistency loss, while Grad-CAM and Integrated Gradients provide multi-scale visual explanations that validate the model’s attention on clinically relevant tumor regions. These findings highlight the potential of our approach for robust and interpretable brain tumor classification. All source code, trained models, and experimental results are publicly available at: https://github.com/MuhammadAhmad7171/Advance-brain-tumor to support full reproducibility.