Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/713
Title: Classifying dysmorphic syndromes by using artificial neural network based hierarchical decision tree
Authors: Özdemir, Merve Erkınay
Telatar, Ziya
Eroğul, Osman
Tunca, Yusuf
10187
Keywords: Dysmorphic syndrome
Classification
Artificial neural network
Hierarchical decision tree
Pre diagnosis
Issue Date: 1-Jun-2018
Publisher: Springer Netherlands
Source: Özdemir, 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.
Abstract: Dysmorphic 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.
URI: https://doi.org/10.1007/s13246-018-0643-x
https://hdl.handle.net/20.500.11851/713
ISSN: 0158-9938
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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