Uyulan, CaglarErguzel, Turker TekinTürk, ÖmerFarhad, ShamsMetin, BarişTarhan, Nevzat2022-12-212022-12-212022Uyulan, C., Erguzel, T. T., Turk, O., Farhad, S., Metin, B., & Tarhan, N. (2022). A Class Activation Map-Based Interpretable Transfer Learning Model for Automated Detection of ADHD from fMRI Data. Clinical EEG and Neurosciencehttps://doi.org/10.1177/15500594221122https://pubmed.ncbi.nlm.nih.gov/36052402/https://hdl.handle.net/20.500.12514/3178A Class Activation Map-Based Interpretable Transfer Learning Model for Automated Detection of ADHD from fMRI Datahttps://www.scopus.com/record/display.uri?eid=2-s2.0-85138308313&origin=SingleRecordEmailAlert&dgcid=raven_sc_affil_en_us_email&txGid=0128eb28694b092589cd0a0f77709986Automatic 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.en10.1177/15500594221122info:eu-repo/semantics/closedAccessattention deficit hyperactivity disorder; class activation maps; convolutional neural network; functional magnetic resonance imaging; transfer learning.ArticleQ3N/AWOS:0008490655000012-s2.0-8513830831336052402