Interpretable attention-deficit hyperactivity disorder diagnosis from functional magnetic resonance imaging via hybrid convolutional neural network–transformer learning and multi-level graph-based comprehensibility

Attention-Deficit Hyperactivity Disorder (ADHD) is a disorder about the brain development with various changes in the brain structure, involving constant diagnosis using predictable neuroimaging analysis. Current deep learning methods appear to show promising performance in computerized ADHD classification using magnetic resonance imaging (MRI); however, their applicability is restricted due to ambiguity. The proposed study is an interpretable hybrid deep learning model for ADHD recognition using fMRI. It uses a three-dimensional Convolutional Neural Network (CNN) to map brain structures and a transformer encoder to analyse transformer modules. At the voxel level, 3D Grad-CAM + + limits the characteristic feature support for ADHD classification. At the interpretative level, cross-layer attention aggregation creates reliable attention patterns during transformer layers, streamlining the identification of related brain structures. A brain connectivity graph is created by extracting region-specific features using a neuroanatomical atlas, and this graph is then inspected using a Graph Neural Network (GNN). GNNExplainer revealed complex brain subnetworks and interregional interactions related to ADHD. Experiments conducted on the ADHD-200 dataset demonstrated that the proposed hybrid framework exceeded the CNN-only and Transformer-only baselines in terms of accuracy, sensitivity, specificity, and AUC. These results show that the proposed approach has better diagnostic efficacy while providing substantial benefits, progressing the feasibility of deep-learning-based ADHD diagnosis from MRI.