Dissecting Glioma Heterogeneity: A Deep Hybrid Graph Convolutional Network With Hinge Attention for Causal‐Effect Explainability

ABSTRACT Brain tumour classification is a critical task in medical imaging that requires accurate and interpretable solutions to assist in clinical decision‐making. In this paper, we present GraphConvNet‐X, a novel hybrid model that integrates convolutional neural networks (CNNs) for spatial feature extraction with graph neural networks (GNNs) that employ a novel hinge attention network (HAN) for robust relational aggregation. Leveraging Explainable Artificial Intelligence (XAI) techniques, such as gradient‐weighted class activation mapping (Grad‐CAM) and SHAP, the model provides transparent insights into its predictions, aligning them with clinically significant regions in magnetic resonance imaging (MRI) scans. The integration of HAN enhances the GNN's ability to selectively focus on the most salient tumour‐related graph features, improving overall discriminative power. The proposed architecture was evaluated on three benchmark datasets, achieving impressive accuracies of 98%, 97% and 96%, surpassing state‐of‐the‐art methods. Extensive experiments have demonstrated its robustness across various configurations and highlighted its adaptability to diverse scenarios. By combining precision, adaptability and interpretability, GraphConvNet‐X sets a new standard for AI‐driven diagnostic tools, showing its potential to revolutionise brain tumour classification and contribute significantly to modern healthcare.