Fuzzy clustering complex-valued neural network to diagnose cirrhosis disease

dc.contributor.authorCeylan, Rahime
dc.contributor.authorCeylan, Murat
dc.contributor.authorOzbay, Yuksel
dc.contributor.authorKara, Sadik
dc.date.accessioned2020-03-26T18:14:44Z
dc.date.available2020-03-26T18:14:44Z
dc.date.issued2011
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractIn this study, fuzzy clustering complex-valued neural network (FCCVNN) was proposed to classify portal vein Doppler signals recorded from 54 patients with cirrhosis and 36 healthy subjects. This proposed neural network is a new model for biomedical pattern classification. The FCCVNN was composed of three phases: fuzzy clustering, calculation of FFT values and complex-valued neural network (CVNN). In first phase, fuzzy clustering was done to reduce the number of segments in training pattern. After that, FFT values of Doppler signals were calculated for pre-processing and then obtained values, which include real and imaginary components, were used as the inputs of the CVNN for classification of Doppler signals. Classification results of FCCVNN were evaluated by the different performance evaluation criterion in literature. It shows that Doppler signals were classified successfully with 100% correct classification rate using the proposed method. Moreover, the rates of sensitivity and specificity were calculated as 100% using FCCVNN method. These results were seen to be appropriate with the expected results that are derived from physician's direct diagnosis. This method would be assisted the physician to make the final decision. (C) 2011 Elsevier Ltd. All rights reserved.en_US
dc.description.sponsorshipSelcuk UniversitySelcuk Universityen_US
dc.description.sponsorshipThis work is supported by the Coordinatorship of Selcuk University's Scientific Research Projects.en_US
dc.identifier.doi10.1016/j.eswa.2011.02.025en_US
dc.identifier.endpage9751en_US
dc.identifier.issn0957-4174en_US
dc.identifier.issn1873-6793en_US
dc.identifier.issue8en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage9744en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.eswa.2011.02.025
dc.identifier.urihttps://hdl.handle.net/20.500.12395/26511
dc.identifier.volume38en_US
dc.identifier.wosWOS:000290237500079en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPERGAMON-ELSEVIER SCIENCE LTDen_US
dc.relation.ispartofEXPERT SYSTEMS WITH APPLICATIONSen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectLiveren_US
dc.subjectCirrhosisen_US
dc.subjectDoppler signalsen_US
dc.subjectComplex-valued artificial neural networken_US
dc.subjectFuzzy c-means clusteringen_US
dc.titleFuzzy clustering complex-valued neural network to diagnose cirrhosis diseaseen_US
dc.typeArticleen_US

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