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In this study, we propose a deep learning architecture to combine functional brain data with two different types of structural brain data to accurately diagnose SZ. Our two-step model consists of deep belief networks (DBN) and a fusing rule to extract and learn hidden features via the combining resting-state fMRI with both sMRI and DTI data. We examine this architecture on all combinations of features extracted from fMRI, MRI and DTI. The experimental results indicate that multi-modal fusion improved classification accuracy between the SZ and the healthy cases. By finding a suitable configuration for algorithm parameters, the proposed method showed an accuracy level of 99.36% for the COBRE dataset and outperformed similar state-of-the-art works.<\/jats:p>","DOI":"10.1142\/s0219691320500885","type":"journal-article","created":{"date-parts":[[2020,12,2]],"date-time":"2020-12-02T09:22:30Z","timestamp":1606900950000},"page":"2050088","source":"Crossref","is-referenced-by-count":5,"title":["A multi-modal fusion of features method based on deep belief networks to diagnosis schizophrenia disease"],"prefix":"10.1142","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3975-5078","authenticated-orcid":false,"given":"Babak","family":"Masoudi","sequence":"first","affiliation":[{"name":"Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sabalan","family":"Daneshvar","sequence":"additional","affiliation":[{"name":"Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran"},{"name":"Department of Electronic and Computer Engineering, College of Engineering, Design and Physical Sciences, Brunel University, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seyed Naser","family":"Razavi","sequence":"additional","affiliation":[{"name":"Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2021,1,16]]},"reference":[{"key":"S0219691320500885BIB001","doi-asserted-by":"publisher","DOI":"10.1142\/S0219691319500231"},{"key":"S0219691320500885BIB002","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.20995"},{"key":"S0219691320500885BIB003","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmr.2011.09.022"},{"key":"S0219691320500885BIB004","doi-asserted-by":"crossref","DOI":"10.2174\/97816080517001100101","volume-title":"Image Processing for Embedded Devices","author":"Battiato S.","year":"2010"},{"key":"S0219691320500885BIB005","doi-asserted-by":"publisher","DOI":"10.1145\/1961189.1961199"},{"key":"S0219691320500885BIB006","doi-asserted-by":"publisher","DOI":"10.1016\/j.pnpbp.2018.06.010"},{"key":"S0219691320500885BIB007","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.20741"},{"key":"S0219691320500885BIB008","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.22935"},{"key":"S0219691320500885BIB009","first-page":"1","volume":"13","author":"Guo S.","year":"2018","journal-title":"PLoS ONE"},{"key":"S0219691320500885BIB010","doi-asserted-by":"publisher","DOI":"10.1186\/s40535-017-0044-3"},{"key":"S0219691320500885BIB011","doi-asserted-by":"publisher","DOI":"10.1016\/j.nicl.2017.10.005"},{"key":"S0219691320500885BIB012","doi-asserted-by":"publisher","DOI":"10.4249\/scholarpedia.5947"},{"key":"S0219691320500885BIB013","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-35289-8_32"},{"key":"S0219691320500885BIB014","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2011.5947700"},{"key":"S0219691320500885BIB016","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2015.05.018"},{"key":"S0219691320500885BIB017","doi-asserted-by":"publisher","DOI":"10.1109\/TNB.2017.2751074"},{"key":"S0219691320500885BIB018","first-page":"1","volume":"18","author":"Mazziotta J. 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