Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/713
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dc.contributor.authorÖzdemir, Merve Erkınay-
dc.contributor.authorTelatar, Ziya-
dc.contributor.authorEroğul, Osman-
dc.contributor.authorTunca, Yusuf-
dc.date.accessioned2019-03-15T08:12:15Z
dc.date.available2019-03-15T08:12:15Z
dc.date.issued2018-06-01
dc.identifier.citationÖzdemir, M. E., Telatar, Z., Eroğul, O., & Tunca, Y. (2018). Classifying dysmorphic syndromes by using artificial neural network based hierarchical decision tree. Australasian physical & engineering sciences in medicine, 41(2), 451-461.en_US
dc.identifier.issn0158-9938
dc.identifier.urihttps://doi.org/10.1007/s13246-018-0643-x-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/713-
dc.description.abstractDysmorphic syndromes have different facial malformations. These malformations are significant to an early diagnosis of dysmorphic syndromes and contain distinctive information for face recognition. In this study we define the certain features of each syndrome by considering facial malformations and classify Fragile X, Hurler, Prader Willi, Down, Wolf Hirschhorn syndromes and healthy groups automatically. The reference points are marked on the face images and ratios between the points’ distances are taken into consideration as features. We suggest a neural network based hierarchical decision tree structure in order to classify the syndrome types. We also implement k-nearest neighbor (k-NN) and artificial neural network (ANN) classifiers to compare classification accuracy with our hierarchical decision tree. The classification accuracy is 50, 73 and 86.7% with k-NN, ANN and hierarchical decision tree methods, respectively. Then, the same images are shown to a clinical expert who achieve a recognition rate of 46.7%. We develop an efficient system to recognize different syndrome types automatically in a simple, non-invasive imaging data, which is independent from the patient’s age, sex and race at high accuracy. The promising results indicate that our method can be used for pre-diagnosis of the dysmorphic syndromes by clinical experts.en_US
dc.language.isoenen_US
dc.publisherSpringer Netherlandsen_US
dc.relation.ispartofAustralasian Physical & Engineering Sciences in Medicineen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDysmorphic syndromeen_US
dc.subjectClassificationen_US
dc.subjectArtificial neural networken_US
dc.subjectHierarchical decision treeen_US
dc.subjectPre diagnosisen_US
dc.titleClassifying Dysmorphic Syndromes by Using Artificial Neural Network Based Hierarchical Decision Treeen_US
dc.typeArticleen_US
dc.departmentFaculties, Faculty of Engineering, Department of Biomedical Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Biyomedikal Mühendisliği Bölümütr_TR
dc.identifier.volume41
dc.identifier.issue2
dc.identifier.startpage451
dc.identifier.endpage461
dc.authorid0000-0002-4640-6570-
dc.identifier.wosWOS:000433915700011en_US
dc.identifier.scopus2-s2.0-8504616466en_US
dc.institutionauthorEroğul, Osman-
dc.identifier.pmid29717432en_US
dc.identifier.doi10.1007/s13246-018-0643-x-
dc.identifier.doi10.1007/s13246-018-0643-x-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ3-
item.openairetypeArticle-
item.languageiso639-1en-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
crisitem.author.dept02.2. Department of Biomedical Engineering-
Appears in Collections:Biyomedikal Mühendisliği Bölümü / Department of Biomedical Engineering
PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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