A Class Activation Map-Based Interpretable Transfer Learning Model for Automated Detection of ADHD from fMRI Data. (March 2023)

• Record Type: Journal Article
• Authors: Uyulan, Caglar; Erguzel, Turker Tekin; Turk, Omer; Farhad, Shams; Metin, Baris; Tarhan, Nevzat
• Is Part Of: Clinical EEG and neuroscience. Volume 54:Number 2(2023)
• Page Start: 151
• Page End: 159
• Publication Date: 2023-03
• Subjects: attention deficit hyperactivity disorder -- functional magnetic resonance imaging -- convolutional neural network -- transfer learning -- class activation maps; Electroencephalography -- Periodicals; Neurosciences -- Periodicals; 616.8047547
• DOI: 10.1177/15500594221122699
• Languages: English
• ISSNs: 1550-0594

Abstract

Automatic 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.