Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6331
Title: Automated diagnostic systems with diverse and composite features for Doppler ultrasound signals
Authors: Güler, İnan
Übeyli, Elif Derya
Keywords: combined neural network (CNN)
composite feature
diverse features
Doppler ultrasound signals
mixture of experts (ME)
modified mixture of experts (MME)
multilayer perceptron neural network (MLP)
probabilistic neural network (PNN)
support vector machine (SVM)
Issue Date: 2006
Publisher: IEEE-Inst Electrical Electronics Engineers Inc
Abstract: In this paper, we present the automated diagnostic systems for Doppler ultrasound signals classification with diverse and composite features and determine their accuracies. We compared the classification accuracies of six different classifiers, namely multilayer perceptron neural network (MLP), combined neural network (CNN), mixture of experts (ME)9 modified mixture of experts (MME), probabilistic neural network (PNN), and support vector machine (SVM), which were trained on diverse or composite features. The present study was conducted with the purpose of answering the question of whether the automated diagnostic systems improve the capability of classification of ophthalmic arterial (OA) and internal carotid arterial (ICA) Doppler signals. Our research demonstrated that the SVM trained on composite feature and the MME trained on diverse features achieved accuracy rates which were higher than that of the other automated diagnostic systems.
URI: https://doi.org/10.1109/TBME.2005.863929
https://hdl.handle.net/20.500.11851/6331
ISSN: 0018-9294
1558-2531
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics 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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