Support vector machines classification based on particle swarm optimization for bone age determination

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dc.contributor.authorGuraksin, Gur Emre
dc.contributor.authorHakli, Huseyin
dc.contributor.authorUguz, Harun
dc.date.accessioned2020-03-26T18:58:32Z
dc.date.available2020-03-26T18:58:32Z
dc.date.issued2014
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractThe evaluation of bone development is a complex and time-consuming task for the physicians since it may cause intraobserver and interobserver differences. In this study, we present a new training algorithm for support vector machines in order to determine the bone age in young children from newborn to 6 years old. By the new algorithm, we aimed to assist the radiologists so as to eliminate the disadvantages of the methods used in bone age determination. To achieve this purpose, primarily feature extraction procedure was performed to the left hand wrist X-ray images by using image processing techniques and the features related with the carpal bones and distal epiphysis of radius bone were obtained. Then these features were used for the input arguments of the classifier. In the classification process, a new training algorithm for support vector machines was proposed by using particle swarm optimization. When training support vector machines, particle swarm optimization was used for generating a new training instance which will represent the whole training set of the related class by using the training set. Finally, these new instances were used as the support vectors and classification process was carried out by using these new instances. The performance of the proposed method was compared with the naive Bayes, k-nearest neighborhood, support vector machines and C4.5 algorithms. As a result, it was determined that the proposed method was found successful than the other methods for bone age determination witha classification performance of 74.87%. (C) 2014 Elsevier B.V. All rights reserved.en_US
dc.identifier.doi10.1016/j.asoc.2014.08.007en_US
dc.identifier.endpage602en_US
dc.identifier.issn1568-4946en_US
dc.identifier.issn1872-9681en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage597en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.asoc.2014.08.007
dc.identifier.urihttps://hdl.handle.net/20.500.12395/31160
dc.identifier.volume24en_US
dc.identifier.wosWOS:000343138500051en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherELSEVIERen_US
dc.relation.ispartofAPPLIED SOFT COMPUTINGen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectSupport vector machinesen_US
dc.subjectBone ageen_US
dc.subjectComputer aided diagnosisen_US
dc.subjectParticle swarm optimizationen_US
dc.titleSupport vector machines classification based on particle swarm optimization for bone age determinationen_US
dc.typeArticleen_US

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