S-ResGCN: A Symmetry-Aware Graph Convolutional Network for MRI-Based Brain Tumor Diagnosis

Early and accurate detection of brain tumors is critical for MRI-based diagnosis. Conven-tional convolutional neural networks often struggle to capture fine-grained details, small or boundary-ambiguous lesions, and hemispheric symmetry patterns. To address these limitations, we propose S-ResGCN, a symmetry-aware framework integrating hierarchical feature extraction, attention mechanisms, and graph-based classification. S-ResGCN em-ploys a ResNet50 backbone to extract multi-level features, with Convolutional Block At-tention Modules applied to intermediate and deep layers to enhance key information and discriminative features. Furthermore, we introduce a novel self-paired regularization to enforce feature consistency between original and horizontally flipped images, improving sensitivity to bilateral symmetric structures. Extracted features are converted into nodes and modeled as a small graph, and a graph convolutional network captures inter-node relationships to generate symmetry-aware predictions. Evaluation on two publicly avail-able brain tumor MRI datasets demonstrates that S-ResGCN achieves average accuracies of 99.83% and 99.37%, with consistently high precision, recall, and F1-scores. These re-sults indicate that S-ResGCN effectively captures fine-grained and symmetric tumor characteristics often overlooked by conventional models, providing a robust and efficient tool for automated, symmetry-aware brain tumor diagnosis.