{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T21:52:57Z","timestamp":1792101177795,"version":"4.4.0"},"reference-count":131,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T00:00:00Z","timestamp":1732838400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Research Foundation of Korea (NRF)","award":["NRF-2022R1G1A1012554"],"award-info":[{"award-number":["NRF-2022R1G1A1012554"]}]},{"DOI":"10.13039\/501100003725","name":"Korean government (MSIT)","doi-asserted-by":"publisher","award":["NRF-2022R1G1A1012554"],"award-info":[{"award-number":["NRF-2022R1G1A1012554"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: Artificial Intelligence (AI) in healthcare employs advanced algorithms to analyze complex and large-scale datasets, mimicking aspects of human cognition. By automating decision-making processes based on predefined thresholds, AI enhances the accuracy and reliability of healthcare data analysis, reducing the need for human intervention. Schizophrenia (SZ), a chronic mental health disorder affecting millions globally, is characterized by symptoms such as auditory hallucinations, paranoia, and disruptions in thought, behavior, and perception. The SZ symptoms can significantly impair daily functioning, underscoring the need for advanced diagnostic tools. Methods: This systematic review has been conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines and examines peer-reviewed studies from the last decade (2015\u20132024) on AI applications in SZ detection as well as classification. The review protocol has been registered in the International Prospective Register of Systematic Reviews (PROSPERO) under registration number: CRD42024612364. Research has been sourced from multiple databases and screened using predefined inclusion criteria. The review evaluates the use of both Machine Learning (ML) and Deep Learning (DL) methods across multiple modalities, including Electroencephalography (EEG), Structural Magnetic Resonance Imaging (sMRI), and Functional Magnetic Resonance Imaging (fMRI). The key aspects reviewed include datasets, preprocessing techniques, and AI models. Results: The review identifies significant advancements in AI methods for SZ diagnosis, particularly in the efficacy of ML and DL models for feature extraction, classification, and multi-modal data integration. It highlights state-of-the-art AI techniques and synthesizes insights into their potential to improve diagnostic outcomes. Additionally, the analysis underscores common challenges, including dataset limitations, variability in preprocessing approaches, and the need for more interpretable models. Conclusions: This study provides a comprehensive evaluation of AI-based methods in SZ prognosis, emphasizing the strengths and limitations of current approaches. By identifying unresolved gaps, it offers valuable directions for future research in the application of AI for SZ detection and diagnosis.<\/jats:p>","DOI":"10.3390\/diagnostics14232698","type":"journal-article","created":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T03:50:57Z","timestamp":1733111457000},"page":"2698","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Schizophrenia Detection and Classification: A Systematic Review of the Last Decade"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-5879-6445","authenticated-orcid":false,"given":"Arghyasree","family":"Saha","sequence":"first","affiliation":[{"name":"Department of Information Technology, Jadavpur University, Jadavpur University Second Campus, Plot No. 8, Salt Lake Bypass, LB Block, Sector III, Salt Lake City, Kolkata-700106, West Bengal, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3307-9865","authenticated-orcid":false,"given":"Seungmin","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Software, Dongseo University, Busan 47011, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0370-5562","authenticated-orcid":false,"given":"Zong Woo","family":"Geem","sequence":"additional","affiliation":[{"name":"College of IT Convergence, Gachon University, Seongnam 13120, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9598-7981","authenticated-orcid":false,"given":"Pawan Kumar","family":"Singh","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Jadavpur University, Jadavpur University Second Campus, Plot No. 8, Salt Lake Bypass, LB Block, Sector III, Salt Lake City, Kolkata-700106, West Bengal, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1007\/s13246-021-01083-2","article-title":"A deep learning approach in automated detection of schizophrenia using scalogram images of EEG signals","volume":"45","author":"Aslan","year":"2022","journal-title":"Phys. Eng. Sci. Med."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Dabiri, M., Dehghani Firouzabadi, F., Yang, K., Barker, P.B., Lee, R.R., and Yousem, D.M. (2022). Neuroimaging in schizophrenia: A review article. Front. Neurosci., 16.","DOI":"10.3389\/fnins.2022.1042814"},{"key":"ref_3","first-page":"e23959","article-title":"Neurobiology of schizophrenia: A comprehensive review","volume":"14","author":"Luvsannyam","year":"2022","journal-title":"Cureus"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Teixeira, F.L., Costa, M.R.E., Abreu, J.P., Cabral, M., Soares, S.P., and Teixeira, J.P. (2023). A narrative review of speech and EEG features for Schizophrenia detection: Progress and challenges. Bioengineering, 10.","DOI":"10.3390\/bioengineering10040493"},{"key":"ref_5","unstructured":"(2023, September 10). Schizophrenia, Available online: https:\/\/www.nimh.nih.gov\/health\/statistics\/schizophrenia."},{"key":"ref_6","unstructured":"(2023, September 10). Schizophrenia. Available online: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/schizophrenia."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2222","DOI":"10.1016\/j.neuroimage.2012.02.018","article-title":"The Human Connectome Project: A data acquisition perspective","volume":"62","author":"Ugurbil","year":"2012","journal-title":"Neuroimage"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1253","DOI":"10.1038\/nn758","article-title":"Genetic influences on brain structure","volume":"4","author":"Thompson","year":"2001","journal-title":"Nat. Neurosci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"eaay6690","DOI":"10.1126\/science.aay6690","article-title":"The genetic architecture of the human cerebral cortex","volume":"367","author":"Grasby","year":"2020","journal-title":"Science"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1038\/s41586-018-0571-7","article-title":"Genome-wide association studies of brain imaging phenotypes in UK Biobank","volume":"562","author":"Elliott","year":"2018","journal-title":"Nature"},{"key":"ref_11","unstructured":"Spitzer, R.L. (1980). Diagnostic and Statistical Manual of Mental Disorders, American Psychiatric Association. [3rd ed.]."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1296","DOI":"10.1093\/schbul\/sbs130","article-title":"A systematic review and meta-analysis of recovery in schizophrenia","volume":"39","author":"Juola","year":"2013","journal-title":"Schizophr. Bull."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.artmed.2019.06.003","article-title":"3D-CNN based discrimination of schizophrenia using resting-state fMRI","volume":"98","author":"Qureshi","year":"2019","journal-title":"Artif. Intell. Med."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1080\/09602011.2016.1189341","article-title":"Personalised treatment for cognitive dysfunction in individuals with schizophrenia spectrum disorders","volume":"28","author":"Medalia","year":"2018","journal-title":"Neuropsychol. Rehabil."},{"key":"ref_15","first-page":"1","article-title":"SzHNN: A novel and scalable deep convolution hybrid neural network framework for schizophrenia detection using multichannel EEG","volume":"71","author":"Sharma","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Sun, J., Cao, R., Zhou, M., Hussain, W., Wang, B., Xue, J., and Xiang, J. (2021). A hybrid deep neural network for classification of schizophrenia using EEG Data. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-83350-6"},{"key":"ref_17","unstructured":"(2023, September 10). Schizophrenia Prevalence. Available online: https:\/\/ourworldindata.org\/grapher\/schizophrenia-prevalence."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1007\/s12021-017-9338-9","article-title":"Multimodal neuroimaging in schizophrenia: Description and dissemination","volume":"15","author":"Aine","year":"2017","journal-title":"Neuroinformatics"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2773","DOI":"10.1038\/s41380-020-0679-7","article-title":"Structural and functional imaging markers for susceptibility to psychosis","volume":"25","author":"Andreou","year":"2020","journal-title":"Mol. Psychiatry"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Oh, S.L., Vicnesh, J., Ciaccio, E.J., Yuvaraj, R., and Acharya, U.R. (2019). Deep convolutional neural network model for automated diagnosis of schizophrenia using EEG signals. Appl. Sci., 9.","DOI":"10.3390\/app9142870"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2227","DOI":"10.1016\/j.clinph.2011.04.011","article-title":"Lempel\u2013Ziv complexity in schizophrenia: A MEG study","volume":"122","author":"Turrero","year":"2011","journal-title":"Clin. Neurophysiol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"192","DOI":"10.3389\/fnhum.2010.00192","article-title":"A hybrid machine learning method for fusing fMRI and genetic data: Combining both improves classification of schizophrenia","volume":"4","author":"Yang","year":"2010","journal-title":"Front. Hum. Neurosci."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Dadgostar, M., Setarehdan, S.K., Shahzadi, S., and Akin, A. (2018). Classification of schizophrenia using SVM via fNIRS. Biomed. Eng. Appl. Basis Commun., 30.","DOI":"10.4015\/S1016237218500084"},{"key":"ref_24","unstructured":"(2023, September 12). EEG Test (Electroencephalogram): Purpose, Procedure, and Risks. Available online: https:\/\/www.simplypsychology.org\/what-is-an-eeg.html."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2345","DOI":"10.1007\/s11831-023-10047-6","article-title":"Deep learning models for diagnosis of schizophrenia using EEG signals: Emerging trends, challenges, and prospects","volume":"31","author":"Ranjan","year":"2024","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Shoeibi, A., Sadeghi, D., Moridian, P., Ghassemi, N., Heras, J., Alizadehsani, R., Khadem, A., Kong, Y., Nahavandi, S., and Zhang, Y.D. (2021). Automatic diagnosis of schizophrenia in EEG signals using CNN-LSTM models. Front. Neuroinform., 15.","DOI":"10.3389\/fninf.2021.777977"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Shameedha Begum, B., Faruk Hossain, M., Jose, J., and Krishnapriya, B. (2023, January 25\u201327). EEG-Based Identification of Schizophrenia Using Deep Learning Techniques. Proceedings of the International Conference on Computation Intelligence and Network Systems 2023, Tokyo, Japan.","DOI":"10.1007\/978-3-031-48984-6_3"},{"key":"ref_28","unstructured":"Yuh, E. (2017). Structural imaging of traumatic brain injury. Youmans and Winn Neurological Surgery, Elsevier."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"20343","DOI":"10.1007\/s11042-022-13809-9","article-title":"Towards artificial intelligence in mental health: A comprehensive survey on the detection of schizophrenia","volume":"82","author":"Tyagi","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1001\/jamapsychiatry.2013.3469","article-title":"Disruption of cortical association networks in schizophrenia and psychotic bipolar disorder","volume":"71","author":"Baker","year":"2014","journal-title":"JAMA Psychiatry"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1176\/appi.ajp.161.1.154","article-title":"Ventricular enlargement in schizophrenia related to volume reduction of the thalamus, striatum, and superior temporal cortex","volume":"161","author":"Gaser","year":"2004","journal-title":"Am. J. Psychiatry"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"617.e1\u2013e9","DOI":"10.1016\/j.neurobiolaging.2010.07.013","article-title":"Normal age-related brain morphometric changes: Nonuniformity across cortical thickness, surface area and gray matter volume?","volume":"33","author":"Lemaitre","year":"2012","journal-title":"Neurobiol. Aging"},{"key":"ref_33","first-page":"46","article-title":"The little thoughts of thinking machines","volume":"17","author":"McCarthy","year":"1983","journal-title":"Psychol. Today"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Mandal, M., Singh, P.K., Ijaz, M.F., Shafi, J., and Sarkar, R. (2021). A tri-stage wrapper-filter feature selection framework for disease classification. Sensors, 21.","DOI":"10.3390\/s21165571"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Chattopadhyay, S., Singh, P.K., Ijaz, M.F., Kim, S., and Sarkar, R. (2023). SnapEnsemFS: A snapshot ensembling-based deep feature selection model for colorectal cancer histological analysis. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-36921-8"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Sahoo, K.K., Ghosh, R., Mallik, S., Roy, A., Singh, P.K., and Zhao, Z. (2023). Wrapper-based deep feature optimization for activity recognition in the wearable sensor networks of healthcare systems. Sci. Rep., 13.","DOI":"10.1038\/s41598-022-27192-w"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"11461","DOI":"10.1007\/s11042-022-14052-y","article-title":"A hybrid deep feature selection framework for emotion recognition from human speeches","volume":"82","author":"Marik","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Bhattacharya, D., Sharma, D., Kim, W., Ijaz, M.F., and Singh, P.K. (2022). Ensem-HAR: An ensemble deep learning model for smartphone sensor-based human activity recognition for measurement of elderly health monitoring. Biosensors, 12.","DOI":"10.3390\/bios12060393"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1035","DOI":"10.1016\/j.biopsych.2008.07.025","article-title":"Multivariate pattern analysis of functional magnetic resonance imaging data reveals deficits in distributed representations in schizophrenia","volume":"64","author":"Yoon","year":"2008","journal-title":"Biol. Psychiatry"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Zemouri, R., Zerhouni, N., and Racoceanu, D. (2019). Deep learning in the biomedical applications: Recent and future status. Appl. Sci., 9.","DOI":"10.3390\/app9081526"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Rakhmatulin, I., Dao, M.S., Nassibi, A., and Mandic, D. (2024). Exploring convolutional neural network architectures for EEG feature extraction. Sensors, 24.","DOI":"10.3390\/s24030877"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1186\/s44147-023-00353-y","article-title":"Detection of epileptic seizure in EEG signals using machine learning and deep learning techniques","volume":"71","author":"Kunekar","year":"2024","journal-title":"J. Eng. Appl. Sci."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Rahul, J., Sharma, D., Sharma, L.D., Nanda, U., and Sarkar, A.K. (2024). A systematic review of EEG based automated schizophrenia classification through machine learning and deep learning. Front. Hum. Neurosci., 18.","DOI":"10.3389\/fnhum.2024.1347082"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1002\/wps.21159","article-title":"Functional magnetic resonance imaging in schizophrenia: Current evidence, methodological advances, limitations and future directions","volume":"23","author":"Voineskos","year":"2024","journal-title":"World Psychiatry"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1007\/s10489-023-05155-6","article-title":"Empowering precision medicine: AI-driven schizophrenia diagnosis via EEG signals: A comprehensive review from 2002\u20132023","volume":"54","author":"Jafari","year":"2024","journal-title":"Appl. Intell."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"4795","DOI":"10.1007\/s12652-023-04536-6","article-title":"Machine learning techniques for the Schizophrenia diagnosis: A comprehensive review and future research directions","volume":"14","author":"Verma","year":"2023","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.schres.2021.05.018","article-title":"Going deep into schizophrenia with artificial intelligence","volume":"245","author":"Estevez","year":"2022","journal-title":"Schizophr. Res."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Sadeghi, D., Shoeibi, A., Ghassemi, N., Moridian, P., Khadem, A., Alizadehsani, R., Teshnehlab, M., Gorriz, J.M., Khozeimeh, F., and Zhang, Y.D. (2022). An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works. Comput. Biol. Med., 146.","DOI":"10.1016\/j.compbiomed.2022.105554"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"102039","DOI":"10.1016\/j.artmed.2021.102039","article-title":"Advanced EEG-based learning approaches to predict schizophrenia: Promises and pitfalls","volume":"114","author":"Barros","year":"2021","journal-title":"Artif. Intell. Med."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Luj\u00e1n, M.\u00c1., Jimeno, M.V., Mateo Sotos, J., Ricarte, J.J., and Borja, A.L. (2021). A survey on eeg signal processing techniques and machine learning: Applications to the neurofeedback of autobiographical memory deficits in schizophrenia. Electronics, 10.","DOI":"10.3390\/electronics10233037"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Lai, J.W., Ang, C.K., Acharya, U.R., and Cheong, K.H. (2021). Schizophrenia: A survey of artificial intelligence techniques applied to detection and classification. Int. J. Environ. Res. Public Health, 18.","DOI":"10.3390\/ijerph18116099"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Steardo Jr, L., Carbone, E.A., De Filippis, R., Pisanu, C., Segura-Garcia, C., Squassina, A., De Fazio, P., and Steardo, L. (2020). Application of support vector machine on fMRI data as biomarkers in schizophrenia diagnosis: A systematic review. Front. Psychiatry, 11.","DOI":"10.3389\/fpsyt.2020.00588"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1605","DOI":"10.2147\/NDT.S202418","article-title":"Machine learning techniques in a structural and functional MRI diagnostic approach in schizophrenia: A systematic review","volume":"15","author":"Carbone","year":"2019","journal-title":"Neuropsychiatr. Dis. Treat."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"n71","DOI":"10.1136\/bmj.n71","article-title":"The PRISMA 2020 statement: An updated guideline for reporting systematic reviews","volume":"372","author":"Page","year":"2021","journal-title":"BMJ"},{"key":"ref_55","unstructured":"(2024, October 15). International Prospective Register of Systematic Reviews (PROSPERO). Available online: https:\/\/www.crd.york.ac.uk\/prospero\/."},{"key":"ref_56","unstructured":"(2024, July 20). The Mind Research Network for Neurodiagnostic Discovery, C.O.B.R.E. Available online: http:\/\/fcon_1000.projects.nitrc.org\/indi\/retro\/cobre.html."},{"key":"ref_57","unstructured":"(2023, September 15). EEG in schizophrenia. Available online: https:\/\/repod.icm.edu.pl\/dataset.xhtml?persistentId=doi:10.18150\/repod.0107441."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Wang, L., Kogan, A., Cobia, D., Alpert, K., Kolasny, A., Miller, M.I., and Marcus, D. (2013). Northwestern University schizophrenia data and software tool (NUSDAST). Front. Neuroinform., 7.","DOI":"10.3389\/fninf.2013.00025"},{"key":"ref_59","unstructured":"Bilder, R., Poldrack, R., Cannon, T., London, E., Freimer, N., Congdon, E., Karlsgodt, K., and Sabb, F. (2020). UCLA Consortium for Neuropsychiatric Phenomics LA5c Study, OpenNeuro. ds000030."},{"key":"ref_60","unstructured":"(2024, July 22). SchizConnect Schizconnect Dataset. Available online: http:\/\/schizconnect.org\/."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1007\/s12021-013-9184-3","article-title":"The MCIC collection: A shared repository of multi-modal, multi-site brain image data from a clinical investigation of schizophrenia","volume":"11","author":"Gollub","year":"2013","journal-title":"Neuroinformatics"},{"key":"ref_62","unstructured":"(2024, July 20). Kaggle MLSP 2014 Dataset. Available online: https:\/\/www.kaggle.com\/c\/mlsp-2014-mri\/data."},{"key":"ref_63","unstructured":"Gorbachevskaya, N.N., and Borisov, S.V. (2023, September 15). EEG of Healthy Adolescents And adolescents with Symptoms of Schizophrenia. Available online: http:\/\/brain.bio.msu.ru\/eeg_schizophrenia.htm."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1093\/schbul\/sbn162","article-title":"Working memory and DLPFC inefficiency in schizophrenia: The FBIRN study","volume":"35","author":"Potkin","year":"2009","journal-title":"Schizophr. Bull."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Olejarczyk, E., and Jernajczyk, W. (2017). Graph-based analysis of brain connectivity in schizophrenia. PloS ONE, 12.","DOI":"10.1371\/journal.pone.0188629"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1016\/j.schres.2016.05.007","article-title":"Machine-learning-based diagnosis of schizophrenia using combined sensor-level and source-level EEG features","volume":"176","author":"Shim","year":"2016","journal-title":"Schizophr. Res."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Khare, S.K., Bajaj, V., Siuly, S., and Sinha, G.R. (2020). Classification of schizophrenia patients through empirical wavelet transformation using electroencephalogram signals. Modelling and Analysis of Active Biopotential Signals in Healthcare, IOP Publishing.","DOI":"10.1088\/978-0-7503-3279-8ch1"},{"key":"ref_68","unstructured":"(2023, September 15). EEG Data from Basic Sensory Task in Schizophrenia. Available online: https:\/\/www.kaggle.com\/datasets\/broach\/button-tone-sz."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Zhang, L. (2020, January 26\u201328). EEG signals feature extraction and artificial neural networks classification for the diagnosis of schizophrenia. Proceedings of the 2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI* CC), Beijing, China.","DOI":"10.1109\/ICCICC50026.2020.9450257"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1007\/s13246-023-01225-8","article-title":"Exploring deep residual network based features for automatic schizophrenia detection from EEG","volume":"46","author":"Siuly","year":"2023","journal-title":"Phys. Eng. Sci. Med."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Barros, C., Roach, B., Ford, J.M., Pinheiro, A.P., and Silva, C.A. (2022). From sound perception to automatic detection of schizophrenia: An EEG-based deep learning approach. Front. Psychiatry, 12.","DOI":"10.3389\/fpsyt.2021.813460"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Buettner, R., Hirschmiller, M., Schlosser, K., R\u00f6ssle, M., Fernandes, M., and Timm, I.J. (2019, January 14\u201316). High-performance exclusion of schizophrenia using a novel machine learning method on EEG data. Proceedings of the 2019 IEEE International Conference on E-Health Networking, Application & Services (HealthCom), Bogota, Colombia. 6p.","DOI":"10.1109\/HealthCom46333.2019.9009437"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1124","DOI":"10.1016\/j.bbe.2020.05.008","article-title":"Schizophrenia detection using MultivariateEmpirical Mode Decomposition and entropy measures from multichannel EEG signal","volume":"40","author":"Krishnan","year":"2020","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_74","unstructured":"Shoeibi, A., Jafari, M., Sadeghi, D., Alizadehsani, R., Alinejad-Rokny, H., Beheshti, A., and Gorriz, J.M. (June, January 31). Early Diagnosis of Schizophrenia in EEG Signals Using One Dimensional Transformer Model. Proceedings of the International Work-Conference on the Interplay Between Natural and Artificial Computation, Olhao, Portugal."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Bagherzadeh, S., Shahabi, M.S., and Shalbaf, A. (2022). Detection of schizophrenia using hybrid of deep learning and brain effective connectivity image from electroencephalogram signal. Comput. Biol. Med., 146.","DOI":"10.1016\/j.compbiomed.2022.105570"},{"key":"ref_76","unstructured":"(2023, December 25). NIMH Repository & Genomics Resource. Available online: https:\/\/www.nimhgenetics.org\/download-tool\/SZ."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1016\/j.nicl.2017.04.027","article-title":"Auditory prediction errors as individual biomarkers of schizophrenia","volume":"15","author":"Taylor","year":"2017","journal-title":"NeuroImage Clin."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"1784","DOI":"10.1109\/TNSRE.2021.3105669","article-title":"Classification of first-episode schizophrenia, chronic schizophrenia and healthy control based on brain network of mismatch negativity by graph neural network","volume":"29","author":"Chang","year":"2021","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Santos Febles, E., Ontivero Ortega, M., Vald\u00e9s Sosa, M., and Sahli, H. (2022). Machine learning techniques for the diagnosis of schizophrenia based on event-related potentials. Front. Neuroinform., 16.","DOI":"10.3389\/fninf.2022.893788"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"98379","DOI":"10.1109\/ACCESS.2024.3428847","article-title":"An explainable deep learning-based method for schizophrenia diagnosis using generative data-augmentation","volume":"12","author":"Saadatinia","year":"2024","journal-title":"IEEE Access"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Phang, C.R., Ting, C.M., Samdin, S.B., and Ombao, H. (2019, January 20\u201323). Classification of EEG-based effective brain connectivity in schizophrenia using deep neural networks. Proceedings of the 2019 9th International IEEE\/EMBS Conference on Neural Engineering (NER), San Francisco, CA, USA.","DOI":"10.1109\/NER.2019.8717087"},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Rajesh, K.N., and Kumar, T.S. (2021, January 26\u201330). Schizophrenia Detection in Adolescents from EEG Signals using Symmetrically weighted Local Binary Patterns. Proceedings of the 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Guadalajara, Mexico.","DOI":"10.1109\/EMBC46164.2021.9630232"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"7913","DOI":"10.1109\/JSEN.2022.3151465","article-title":"A new signal to image mapping procedure and convolutional neural networks for efficient schizophrenia detection in EEG recordings","volume":"22","author":"Sobahi","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.neuroimage.2016.02.079","article-title":"Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls","volume":"145","author":"Arbabshirani","year":"2017","journal-title":"Neuroimage"},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Qureshi, M.N., Oh, J., Cho, D., Jo, H.J., and Lee, B. (2017). Multimodal discrimination of schizophrenia using hybrid weighted feature concatenation of brain functional connectivity and anatomical features with an extreme learning machine. Front. Neuroinform., 11.","DOI":"10.3389\/fninf.2017.00059"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1007\/s10334-018-0674-z","article-title":"Segmentation and texture analysis of structural biomarkers using neighborhood-clustering-based level set in MRI of the schizophrenic brain","volume":"31","author":"Latha","year":"2018","journal-title":"Magn. Reson. Mater. Phys. Biol. Med."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.pscychresns.2018.12.013","article-title":"Resting-state anticorrelated networks in Schizophrenia","volume":"284","author":"Ramkiran","year":"2019","journal-title":"Psychiatry Res. Neuroimaging"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"6405930","DOI":"10.1155\/2020\/6405930","article-title":"Detecting abnormal brain regions in schizophrenia using structural MRI via machine learning","volume":"2020","author":"Chen","year":"2020","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Guo, Y., Qiu, J., and Lu, W. (2020). Support vector machine-based schizophrenia classification using morphological information from amygdaloid and hippocampal subregions. Brain Sci., 10.","DOI":"10.3390\/brainsci10080562"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"1185","DOI":"10.1109\/JBHI.2022.3168357","article-title":"Diagnosis of schizophrenia: A comprehensive evaluation","volume":"27","author":"Tanveer","year":"2022","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.schres.2019.05.044","article-title":"Identifying schizophrenia subgroups using clustering and supervised learning","volume":"214","author":"Talpalaru","year":"2019","journal-title":"Schizophr. Res."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"944","DOI":"10.1002\/hbm.24423","article-title":"Using deep autoencoders to identify abnormal brain structural patterns in neuropsychiatric disorders: A large-scale multi-sample study","volume":"40","author":"Pinaya","year":"2019","journal-title":"Hum. Brain Mapp."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1016\/j.schres.2016.08.027","article-title":"Improved individualized prediction of schizophrenia in subjects at familial high risk, based on neuroanatomical data, schizotypal and neurocognitive features","volume":"181","author":"Zarogianni","year":"2017","journal-title":"Schizophr. Res."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"29651","DOI":"10.1007\/s11042-017-5470-7","article-title":"MMM: Classification of schizophrenia using multi-modality multi-atlas feature representation and multi-kernel learning","volume":"77","author":"Liu","year":"2018","journal-title":"Multimed. Tools Appl."},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Nimkar, A.V., and Kubal, D.R. (2018, January 13\u201314). Optimization of schizophrenia diagnosis prediction using machine learning techniques. Proceedings of the 2018 4th International Conference on Computer and Information Sciences (ICCOINS), Kuala Lumpur, Malaysia. 6p.","DOI":"10.1109\/ICCOINS.2018.8510599"},{"key":"ref_96","unstructured":"Nguyen, H., Morris, R.W., Harris, A.W., Korgoankar, M.S., and Ramos, F. (2018). Correcting differences in multi-site neuroimaging data using Generative Adversarial Networks. arXiv."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1080\/0952813X.2018.1563636","article-title":"A deep learning approach for diagnosing schizophrenic patients","volume":"31","author":"Srinivasagopalan","year":"2019","journal-title":"J. Exp. Theor. Artif. Intell."},{"key":"ref_98","unstructured":"(2024, July 20). B-Snip\u2014Nationwide Bi-Polar and Schizophrenia Study. Available online: https:\/\/b-snip.org\/."},{"key":"ref_99","first-page":"819","article-title":"Addressing inaccurate nosology in mental health: A multilabel data cleansing approach for detecting label noise from structural magnetic resonance imaging data in mood and psychosis disorders","volume":"5","author":"Rokham","year":"2020","journal-title":"Biol. Psychiatry Cogn. Neurosci. Neuroimaging"},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Oh, J., Oh, B.L., Lee, K.U., Chae, J.H., and Yun, K. (2020). Identifying schizophrenia using structural MRI with a deep learning algorithm. Front. Psychiatry, 11.","DOI":"10.3389\/fpsyt.2020.00016"},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Zhang, J., Rao, V.M., Tian, Y., Yang, Y., Acosta, N., Wan, Z., Lee, P.Y., Zhang, C., Kegeles, L.S., and Small, S.A. (2023). Detecting schizophrenia with 3D structural brain MRI using deep learning. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-41359-z"},{"key":"ref_102","unstructured":"(2024, July 20). Downloads\u2014NAMIC Wiki. Available online: https:\/\/www.na-mic.org\/wiki\/Downloads."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1007\/s40846-017-0355-9","article-title":"Diagnosis of schizophrenia disorder in MR brain images using multi-objective BPSO based feature selection with fuzzy SVM","volume":"38","author":"Manohar","year":"2018","journal-title":"J. Med. Biol. Eng."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"23401","DOI":"10.1007\/s11042-020-09122-y","article-title":"Diagnosis of schizophrenia from R-fMRI data using Ripplet transform and OLPP","volume":"79","author":"Sartipi","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1016\/j.irbm.2020.10.006","article-title":"Combined metaheuristic algorithm and radiomics strategy for the analysis of neuroanatomical structures in schizophrenia and schizoaffective disorders","volume":"42","author":"Latha","year":"2021","journal-title":"IRBM"},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Man, W., Ding, H., Chai, C., An, X., Liu, F., Qin, W., and Yu, C. (2021, January 26\u201330). Brain age gap as a potential biomarker for schizophrenia: A multi-site structural MRI study. Proceedings of the 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Guadalajara, Mexico.","DOI":"10.1109\/EMBC46164.2021.9631085"},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"De Pierrefeu, A., L\u00f6fstedt, T., Laidi, C., Hadj-Selem, F., Leboyer, M., Ciuciu, P., Houenou, J., and Duchesnay, E. (2018, January 12\u201314). Interpretable and stable prediction of schizophrenia on a large multisite dataset using machine learning with structured sparsity. Proceedings of the 2018 International Workshop on Pattern Recognition in Neuroimaging (PRNI), Singapore. 4p.","DOI":"10.1109\/PRNI.2018.8423946"},{"key":"ref_108","unstructured":"(2024, July 22). Schizophrenia\u2014Institute of Mental Health. Available online: https:\/\/www.imh.com.sg\/Mental-Health-Resources\/Conditions-and-Challenges\/Pages\/Schizophrenia.aspx."},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Chilla, G.S., Yeow, L.Y., Chew, Q.H., Sim, K., and Prakash, K.B. (2022). Machine learning classification of schizophrenia patients and healthy controls using diverse neuroanatomical markers and Ensemble methods. Sci. Rep., 12.","DOI":"10.1038\/s41598-022-06651-4"},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Hu, M., Sim, K., Zhou, J.H., Jiang, X., and Guan, C. (2020, January 20\u201324). Brain MRI-based 3D convolutional neural networks for classification of schizophrenia and controls. Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada.","DOI":"10.1109\/EMBC44109.2020.9176610"},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Kadry, S., Taniar, D., Dama\u0161evi\u010dius, R., and Rajinikanth, V. (2021, January 25\u201327). Automated detection of schizophrenia from brain MRI slices using optimized deep-features. Proceedings of the 2021 Seventh International Conference on Bio Signals, Images, and Instrumentation (ICBSII), Chennai, India. 5p.","DOI":"10.1109\/ICBSII51839.2021.9445133"},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Xiang, Y., Wang, J., Tan, G., Wu, F.X., and Liu, J. (2020). Schizophrenia identification using multi-view graph measures of functional brain networks. Front. Bioeng. Biotechnol., 7.","DOI":"10.3389\/fbioe.2019.00479"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1007\/s11760-022-02229-9","article-title":"Diagnosis of schizophrenia using brain resting-state fMRI with activity maps based on deep learning","volume":"17","author":"Ghanbari","year":"2023","journal-title":"Signal Image Video Process."},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Nallusamy, K., and Easwarakumar, K.S. (2023). Classifying schizophrenic and controls from fMRI data using graph theoretic framework and community detection. Netw. Model. Anal. Health Inform. Bioinform., 12.","DOI":"10.1007\/s13721-023-00415-4"},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"3963","DOI":"10.1007\/s11042-017-4404-8","article-title":"fMRI based computer aided diagnosis of schizophrenia using fuzzy kernel feature extraction and hybrid feature selection","volume":"77","author":"Juneja","year":"2018","journal-title":"Multimed. Tools Appl."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"26991","DOI":"10.1007\/s11042-018-5901-0","article-title":"Bi-objective approach for computer-aided diagnosis of schizophrenia patients using fMRI data","volume":"77","author":"Chatterjee","year":"2018","journal-title":"Multimed. Tools Appl."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1016\/j.schres.2017.04.028","article-title":"Abnormal neural activity as a potential biomarker for drug-naive first-episode adolescent-onset schizophrenia with coherence regional homogeneity and support vector machine analyses","volume":"192","author":"Liu","year":"2018","journal-title":"Schizophr. Res."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.schres.2017.05.038","article-title":"Abnormal regional homogeneity as a potential imaging biomarker for adolescent-onset schizophrenia: A resting-state fMRI study and support vector machine analysis","volume":"192","author":"Wang","year":"2018","journal-title":"Schizophr. Res."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12938-018-0464-x","article-title":"Non-negative discriminative brain functional connectivity for identifying schizophrenia on resting-state fMRI","volume":"17","author":"Zhu","year":"2018","journal-title":"Biomed. Eng. Online"},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Salvador, R., Canales-Rodr\u00edguez, E., Guerrero-Pedraza, A., Sarr\u00f3, S., Tordesillas-Guti\u00e9rrez, D., Maristany, T., Crespo-Facorro, B., McKenna, P., and Pomarol-Clotet, E. (2019). Multimodal integration of brain images for MRI-based diagnosis in schizophrenia. Front. Neurosci., 13.","DOI":"10.3389\/fnins.2019.01203"},{"key":"ref_121","doi-asserted-by":"crossref","unstructured":"Qiu, Y., Lin, Q.H., Kuang, L.D., Zhao, W.D., Gong, X.F., Cong, F., and Calhoun, V.D. (2019, January 10\u201312). Classification of schizophrenia patients and healthy controls using ICA of complex-valued fMRI data and convolutional neural networks. Proceedings of the Advances in Neural Networks\u2013ISNN 2019: 16th International Symposium on Neural Networks, ISNN 2019, Moscow, Russia. Proceedings, Part II.","DOI":"10.1007\/978-3-030-22808-8_53"},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Niu, Y.W., Lin, Q.H., Qiu, Y., Kuang, L.D., and Calhoun, V.D. (2019, January 14\u201319). Sample augmentation for classification of schizophrenia patients and healthy controls using ica of fmri data and convolutional neural networks. Proceedings of the 2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP), Marrakesh, Morocco.","DOI":"10.1109\/ICICIP47338.2019.9012169"},{"key":"ref_123","unstructured":"Yang, Y., Cui, Y., Xu, K., Liu, B., Song, M., Chen, J., Wang, H., Chen, Y., Guo, H., and Li, P. (2017, January 13\u201314). Distributed functional connectivity impairment in schizophrenia: A multi-site study. Proceedings of the 2nd IET International Conference on Biomedical Image and Signal Processing (ICBISP 2017), Wuhan, China. 6p."},{"key":"ref_124","unstructured":"(2024, July 22). Conte Center for the Neuroscience of Mental Disorder (CCNMD), Available online: https:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC5873464\/."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1007\/s10278-017-0020-4","article-title":"Differences between schizophrenic and normal subjects using network properties from fMRI","volume":"31","author":"Bae","year":"2018","journal-title":"J. Digit. Imaging"},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"1119","DOI":"10.1002\/hbm.24863","article-title":"Integrating machining learning and multimodal neuroimaging to detect schizophrenia at the level of the individual","volume":"41","author":"Lei","year":"2020","journal-title":"Hum. Brain Mapp."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.jneumeth.2018.08.027","article-title":"A kernel machine method for detecting higher order interactions in multimodal datasets: Application to schizophrenia","volume":"309","author":"Alam","year":"2018","journal-title":"J. Neurosci. Methods"},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"20093","DOI":"10.1109\/ACCESS.2020.2968634","article-title":"Deep principal correlated auto-encoders with application to imaging and genomics data integration","volume":"8","author":"Li","year":"2020","journal-title":"IEEE Access"},{"key":"ref_129","doi-asserted-by":"crossref","unstructured":"Jain, P., Sao, A.K., and Minhas, A.S. (2021, January 26\u201330). Analyzing the effect of resolution of network nodes on the resting state functional connectivity maps of schizophrenic human brains. Proceedings of the 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Guadalajara, Mexico.","DOI":"10.1109\/EMBC46164.2021.9630822"},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"24757","DOI":"10.1007\/s11042-020-09183-z","article-title":"Impact of ageing on the brain regions of the schizophrenia patients: An fMRI study using evolutionary approach","volume":"79","author":"Chatterjee","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_131","doi-asserted-by":"crossref","unstructured":"Liu, P., Mei, X., and Fei, S. (2019, January 27\u201330). A compound classification model for schizophrenia based on brain fmri and network modelling. Proceedings of the 2019 Chinese control conference (CCC), Guanzhou, China.","DOI":"10.23919\/ChiCC.2019.8865960"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/14\/23\/2698\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:42:51Z","timestamp":1760114571000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/14\/23\/2698"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,29]]},"references-count":131,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["diagnostics14232698"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics14232698","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,29]]}}}