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

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dc.authorid0000-0002-0060-1880en_US
dc.contributor.authorUyulan, Caglar
dc.contributor.authorErguzel, Turker Tekin
dc.contributor.authorTürk, Ömer
dc.contributor.authorFarhad, Shams
dc.contributor.authorMetin, Bariş
dc.contributor.authorTarhan, Nevzat
dc.date.accessioned2022-12-21T06:00:49Z
dc.date.available2022-12-21T06:00:49Z
dc.date.issued2022en_US
dc.departmentMAÜ, Meslek Yüksekokulları, Mardin Meslek Yüksekokulu, Bilgisayar Teknolojileri Bölümüen_US
dc.description.abstractAutomatic 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.en_US
dc.identifier.citationUyulan, 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 Neuroscienceen_US
dc.identifier.doi10.1177/15500594221122en_US
dc.identifier.pmid36052402en_US
dc.identifier.scopus2-s2.0-85138308313en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1177/15500594221122
dc.identifier.urihttps://pubmed.ncbi.nlm.nih.gov/36052402/
dc.identifier.urihttps://hdl.handle.net/20.500.12514/3178
dc.identifier.uriA Class Activation Map-Based Interpretable Transfer Learning Model for Automated Detection of ADHD from fMRI Data
dc.identifier.urihttps://www.scopus.com/record/display.uri?eid=2-s2.0-85138308313&origin=SingleRecordEmailAlert&dgcid=raven_sc_affil_en_us_email&txGid=0128eb28694b092589cd0a0f77709986
dc.identifier.wosWOS:000849065500001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherSage Journalsen_US
dc.relation.ispartofClinical EEG and Neuroscienceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectattention deficit hyperactivity disorder; class activation maps; convolutional neural network; functional magnetic resonance imaging; transfer learning.en_US
dc.titleA Class Activation Map-Based Interpretable Transfer Learning Model for Automated Detection of ADHD from fMRI Dataen_US
dc.typeArticleen_US

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