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

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dc.authoridLi, Yan/0000-0002-4694-4926
dc.authoridSiuly, Siuly/0000-0003-2491-0546
dc.authoridwen, peng/0000-0003-0939-9145
dc.authorwosidWen, Peng/F-2802-2010
dc.contributor.authorSiuly, Siuly
dc.contributor.authorLi, Yan
dc.contributor.authorWen, Peng
dc.contributor.authorAlcin, Omer Faruk
dc.date.accessioned2024-08-04T21:01:04Z
dc.date.available2024-08-04T21:01:04Z
dc.date.issued2022
dc.departmentİnönü Üniversitesien_US
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.en_US
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]en_US
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).en_US
dc.identifier.doi10.1155/2022/1992596
dc.identifier.issn1687-5265
dc.identifier.issn1687-5273
dc.identifier.pmid36120676en_US
dc.identifier.urihttps://doi.org/10.1155/2022/1992596
dc.identifier.urihttps://hdl.handle.net/11616/104073
dc.identifier.volume2022en_US
dc.identifier.wosWOS:000874824100006en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherHindawi Ltden_US
dc.relation.ispartofComputational Intelligence and Neuroscienceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectEegen_US
dc.subjectClassificationen_US
dc.subjectHealthen_US
dc.titleSchizoGoogLeNet: The GoogLeNet-Based Deep Feature Extraction Design for Automatic Detection of Schizophreniaen_US
dc.typeArticleen_US

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