SchizoGoogLeNet: The GoogLeNet-Based Deep Feature Extraction Design for Automatic Detection of Schizophrenia

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dc.authoridSiuly, Siuly/0000-0003-2491-0546|Li, Yan/0000-0002-4694-4926|wen, peng/0000-0003-0939-9145|Alcin, Omer Faruk/0000-0002-2917-3736;
dc.contributor.authorSiuly, Siuly
dc.contributor.authorLi, Yan
dc.contributor.authorWen, Peng
dc.contributor.authorAlcin, Omer Faruk
dc.date.accessioned2025-10-24T18:09:27Z
dc.date.available2025-10-24T18:09:27Z
dc.date.issued2022
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractSchizophrenia (SZ) is a severe and prolonged disorder of the human brain where people interpret reality in an abnormal way. Traditional methods of SZ detection are based on handcrafted feature extraction methods (manual process), which are tedious and unsophisticated, and also limited in their ability to balance efficiency and accuracy. To solve this issue, this study designed a deep learning-based feature extraction scheme involving the GoogLeNet model called SchizoGoogLeNet that can efficiently and automatically distinguish schizophrenic patients from healthy control (HC) subjects using electroencephalogram (EEG) signals with improved performance. The proposed framework involves multiple stages of EEG data processing. First, this study employs the average filtering method to remove noise and artifacts from the raw EEG signals to improve the signal-to-noise ratio. After that, a GoogLeNet model is designed to discover significant hidden features from denoised signals to identify schizophrenic patients from HC subjects. Finally, the obtained deep feature set is evaluated by the GoogleNet classifier and also some renowned machine learning classifiers to find a sustainable classification method for the obtained deep feature set. Experimental results show that the proposed deep feature extraction model with a support vector machine performs the best, producing a 99.02% correct classification rate for SZ, with an overall accuracy of 98.84%. Furthermore, our proposed model outperforms other existing methods. The proposed design is able to accurately discriminate SZ from HC, and it will be useful for developing a diagnostic tool for SZ detection.
dc.description.sponsorshipKaggle EEG data collection team for the dataset: EEG data from basic sensory task in Schizophrenia; National Institute of Mental Health; [5R01MH058262-16]
dc.description.sponsorshipAcknowledgmentsThe authors would like to thank Kaggle EEG data collection team for the dataset: EEG data from basic sensory task in Schizophrenia. Funding for the data collection was supported by National Institute of Mental Health (Project Number: 5R01MH058262-16).
dc.identifier.doi10.1155/2022/1992596
dc.identifier.issn1687-5265
dc.identifier.issn1687-5273
dc.identifier.pmid36120676
dc.identifier.urihttps://doi.org/10.1155/2022/1992596
dc.identifier.urihttps://hdl.handle.net/20.500.12899/3656
dc.identifier.volume2022
dc.identifier.wosWOS:000874824100006
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherHindawi Ltd
dc.relation.ispartofComputational Intelligence And Neuroscience
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20251023
dc.subjectEeg; Classification; Health
dc.titleSchizoGoogLeNet: The GoogLeNet-Based Deep Feature Extraction Design for Automatic Detection of Schizophrenia
dc.typeArticle

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