Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6198
Title: Adaptive neuro-fuzzy inference system employing wavelet coefficients for detection of ophthalmic arterial disorders
Authors: Übeyli, Elif Derya
Keywords: adaptive neuro-fuzzy inference system (ANFlS)
fuzzy logic
discrete wavelet transform
Doppler signal
ophthalmic arterial disorders
Issue Date: 2008
Publisher: Pergamon-Elsevier Science Ltd
Abstract: In this study, a new approach based on adaptive neuro-fuzzy inference system (ANFIS) is presented for detection of ophthalmic arterial (OA) disorders. Decision making was performed in two stages: feature extraction using the discrete wavelet transform (DWT) and the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Four types of OA Doppler signals were used as input patterns of the four ANFIS classifiers. To improve diagnostic accuracy, the fifth ANFIS classifier (combining ANFIS) was trained using the outputs of the four ANFIS classifiers as input data. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the impacts of features on the detection of OA disorders were obtained through analysis of the ANFIS. The performance of the ANFIS classifier was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS classifier has potential in detecting the OA disorders. (c) 2007 Elsevier Ltd. All rights reserved.
URI: https://doi.org/10.1016/j.eswa.2007.02.020
https://hdl.handle.net/20.500.11851/6198
ISSN: 0957-4174
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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