A Class Activation Map-Based Interpretable Transfer Learning Model for Automated Detection of ADHD from fMRI Data

Uyulan, CaglarTurk, OmerTarhan, NevzatErguzel, Turker TekinFarhad, ShamsMetin, Baris2026-04-162026-04-1620232169-52021550-0594https://hdl.handle.net/20.500.12514/10611https://doi.org/10.1177/15500594221122699Automatic detection of Attention Deficit Hyperactivity Disorder (ADHD) based on the functional Magnetic Resonance Imaging (fMRI) through Deep Learning (DL) is becoming a quite useful methodology due to the curse of-dimensionality problem of the data is solved. Also, this method proposes an invasive and robust solution to the variances in data acquisition and class distribution imbalances. In this paper, a transfer learning approach, specifically ResNet-50 type pre-trained 2D-Convolutional Neural Network (CNN) was used to automatically classify ADHD and healthy children. The results demonstrated that ResNet-50 architecture with 10-k cross-validation (CV) achieves an overall classification accuracy of 93.45%. The interpretation of the results was done via the Class Activation Map (CAM) analysis which showed that children with ADHD differed from controls in a wide range of brain areas including frontal, parietal and temporal lobes.en10.1177/15500594221122699info:eu-repo/semantics/closedAccessTransfer LearningAttention Deficit Hyperactivity DisorderFunctional Magnetic Resonance ImagingConvolutional Neural NetworkClass Activation MapsA Class Activation Map-Based Interpretable Transfer Learning Model for Automated Detection of ADHD from fMRI DataArticle2-s2.0-85138308313