Machine and Deep Learning Based Detection of Attention Deficit Hyperactivity Disorder

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dc.contributor.authorCicek G.
dc.contributor.authorAkan A.
dc.date.accessioned2024-03-13T10:00:55Z
dc.date.available2024-03-13T10:00:55Z
dc.date.issued2023
dc.departmentİstanbul Beykent Üniversitesien_US
dc.description2023 Innovations in Intelligent Systems and Applications Conference, ASYU 2023 -- 11 October 2023 through 13 October 2023 -- -- 194153en_US
dc.description.abstractAttention Deficit Hyperactivity Disorder (ADHD) is a brain disease that can cause academic, social and psychiatric problems. Structural Magnetic resonance imaging (structural MR) is a critical diagnostic tool used to examine brain anatomy and pathology. In this study, an objective ADHD detection model was developed with Machine Learning (ML) and Deep Learning (DL) methods using structural MR images. Gray and white matter is an important parameter in the diagnosis of many psychiatric diseases. An algorithm has been developed for the detection of slices in which gray and white matter appear complete and clear. While the slices determined by the proposed algorithm are assigned to one dataset, all slices of the structural MR image are assigned to the other dataset. Different feature sets were created by characterizing structural MR images with ML (LBP and Haralick) and DL methods (AlexNet, VggNet, ResNet, SqueezeNet and InceptionResNet). High classification performances were observed in the characterization of the dataset containing the selected slices with ML and DL algorithms. High classification performances were observed with LBP and Haralick, which were especially successful in capturing changes in texture. © 2023 IEEE.en_US
dc.description.sponsorship2017-ÖNAP-MÜMF-0002, 2019-GAP-MÜMF-003en_US
dc.description.sponsorshipFUNDING This work was supported by Izmir Katip Celebi University Scientific Research Projects Coordination Unit: Project numbers 2019-GAP-MÜMF-003 and 2017-ÖNAP-MÜMF-0002.en_US
dc.identifier.doi10.1109/ASYU58738.2023.10296678
dc.identifier.isbn9798350306590
dc.identifier.scopus2-s2.0-85178271442en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1109/ASYU58738.2023.10296678
dc.identifier.urihttps://hdl.handle.net/20.500.12662/2858
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2023 Innovations in Intelligent Systems and Applications Conference, ASYU 2023en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAttention Deficit Hyperactivity Disorder (ADHD)en_US
dc.subjectDeep Learning (DL)en_US
dc.subjectHaralick Featuresen_US
dc.subjectLocal Binary Patterns (LBP)en_US
dc.subjectMachine Learning (ML)en_US
dc.subjectStructural Magnetic Resonance Imaging (Structural MR)en_US
dc.titleMachine and Deep Learning Based Detection of Attention Deficit Hyperactivity Disorderen_US
dc.typeConference Objecten_US

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