Convolutional neural networks can diagnose schizophrenia

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dc.authorid0000-0003-0978-9653
dc.authorid0000-0002-9889-9952
dc.authorid0000-0002-3087-541X
dc.authorid0000-0002-2150-4756
dc.contributor.authorDegirmenci, Murside
dc.contributor.authorSurucu, Murat
dc.contributor.authorPerc, Matjaz
dc.contributor.authorIsler, Yalcin
dc.date.accessioned2026-01-24T12:31:16Z
dc.date.available2026-01-24T12:31:16Z
dc.date.issued2025
dc.departmentAlanya Alaaddin Keykubat Üniversitesi
dc.description.abstractSchizophrenia is a severe mental disorder that affects how individuals think, perceive, and behave, often making accurate and timely diagnosis a significant challenge for clinicians. Traditional diagnostic approaches, such as interviews and psychological tests, have limitations in capturing the complex neurological underpinnings of the condition. In recent years, machine learning and deep learning techniques have shown promise in improving diagnostic accuracy across a variety of medical domains. However, relatively few studies have applied these methods to schizophrenia diagnosis, despite their potential. In this study, we investigate whether convolutional neural networks can effectively diagnose schizophrenia using publicly available EEG data. We achieved classification accuracies of 98.26% in subject-independent settings and 91.21% in subject-dependent settings on the test data, using a fully connected layer based on a Multi-Layer Perceptron classifier. These results appear promising when compared to the current state of the art.
dc.description.sponsorshipSlovenian Research and Innovation Agency (Javna agencija za znanstvenoraziskovalno in inovacijsko dejavnost Republike Slovenije) [P1-0403]
dc.description.sponsorshipMatja & zcaron; Perc was supported by the Slovenian Research and Innovation Agency (Javna agencija za znanstvenoraziskovalno in inovacijsko dejavnost Republike Slovenije) (Grant No. P1-0403) .
dc.identifier.doi10.1016/j.jocs.2025.102634
dc.identifier.issn1877-7503
dc.identifier.issn1877-7511
dc.identifier.scopus2-s2.0-105007437205
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.jocs.2025.102634
dc.identifier.urihttps://hdl.handle.net/20.500.12868/5745
dc.identifier.volume90
dc.identifier.wosWOS:001509106600001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofJournal of Computational Science
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20260121
dc.subjectSchizophrenia
dc.subjectElectroencephalogram
dc.subjectClassification
dc.subjectConvolutional neural networks
dc.subjectMachine learning
dc.titleConvolutional neural networks can diagnose schizophrenia
dc.typeArticle

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