Hybrid Vi+ECNN framework for advanced ADHD diagnostic accuracy in medical imaging.

Attention-Deficit/Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder that imposes significant personal and societal burdens. Traditional diagnostic approaches, which rely on behavioral assessments, are susceptible to subjectivity and variability, underscoring the need for objective and automated diagnostic tools. This study develops an ADHD-specific, biologically informed multi-stream deep learning framework for pediatric brain MRI classification, in which a Vision Transformer (ViT) and an Enhanced Convolutional Neural Network (ECNN) are integrated with Raw MRI, Phase Spectrum Transform (PST), and Quantile Histogram Equalization with Denoising (QHED) representations to capture complementary global and local neuroanatomical characteristics. The architecture leverages complementary modeling capacities by combining global contextual representations from ViT with localized discriminative features extracted by ECNN across a biologically informed multi-stream preprocessing strategy, including Raw MRI to preserve global anatomy, Phase Spectrum Transform (PST) to highlight cortical boundary irregularities, and Quantile Histogram Equalization with Denoising (QHED) to enhance subtle gray-white matter contrasts. Experimental evaluations conducted on a stratified pediatric MRI dataset demonstrated that the proposed ViT+ECNN model achieved a classification accuracy of 99.4%, precision of 99.3%, recall of 99.5%, and an F1-score of 0.99, substantially outperforming standalone ViT and ECNN configurations. These findings indicate that hybrid transformer-convolutional models can substantially enhance diagnostic accuracy and offer a promising approach for supporting early identification and intervention in ADHD.