Authors - Shreya Shukla, Mishti Kukreja, Ruchika Katariya Abstract - Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder with a prevalence rate ranging between 5 to 7.2% among children and between 2.5 to 6.7% among adults worldwide. In spite of its high prevalence rate, its diagnosis still relies mostly on clinical ratings that have the tendency to show inter-rater differences and confusions with symptoms of other conditions associated with ADHD. Neuroimaging methods, particularly rs-fMRI and sMRI, offer an innovative approach towards providing objective measures for understanding the neurobiological underpinnings of ADHD. This paper offers a systematic narrative review of deep learning methods for ADHD classification using fMRI/sMRI data from 2012 to 2025, with a specific focus on the recent period from 2021 to 2025 characterized by architectural diversity. We classify the literature into three main streams according to the neural networks adopted: (1) Convolutional Neural Networks (CNNs), which involve 2D CNNs, 3D CNNs, residual CNNs, dense CNNs, attention-based CNNs, and graph-based CNNs; (2) Vision Transformers (ViTs), which encompass conventional ViTs, Swin transformers, self-supervised ViTs, multi-modal ViTs, and brain foundation model ViTs; and (3) hybrid CNN-ViT models, which combine both local and global context representations. This work highlights the problems of heterogeneity among multiple sites, inconsistent evaluations, fairness, efficient inference, and clinical deployment. Note: This review does not follow the guidelines for systematic reviews (PRISMA 2020). It is an organized narrative review. Numerical comparisons between different works should be considered approximate due to variations in training/testing sets and data preprocessing.
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