Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/7123
Title: Neural network analysis of ophthalmic arterial doppler signals with Uveitis disease
Authors: Güler, İnan
Übeyli, Derya Elif
Keywords: Doppler ultrasound
spectral analysis
multilayer perceptron neural network
quick propagation
Uveitis disease
ophthalmic artery
Issue Date: 2005
Publisher: Springer
Abstract: Doppler ultrasound is known as a reliable technique, which demonstrates the flow characteristics and resistance of ophthalmic arteries. In this study, ophthalmic arterial Doppler signals were obtained from 95 subjects - that 45 of them had suffered from Uveitis disease and the rest of them had been healthy subjects. Multilayer perceptron neural network (MLPNN) employing quick propagation training algorithm was used to detect the presence of Uveitis disease. Spectral analysis of ophthalmic arterial Doppler signals was performed by autoregressive moving average (ARMA) method for determining the MLPNN inputs. The MLPNN was trained with training set, cross validated with cross validation set and tested with testing set. All these data sets were obtained from ophthalmic arteries of healthy subjects and subjects suffering from Uveitis disease. Performance indicators and statistical measures were used for evaluating the MLPNN. The correct classification rate was 95.83% for healthy subjects and 91.30% for subjects suffering from Uveitis disease. Based on the accuracy of the MLPNN detections, it can be mentioned that the classification of ophthalmic arterial Doppler signals with Uveitis disease is feasible by the MLPNN employing quick propagation training algorithm.
URI: https://doi.org/10.1007/s00521-005-0473-0
https://hdl.handle.net/20.500.11851/7123
ISSN: 0941-0643
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

Show full item record

CORE Recommender

SCOPUSTM   
Citations

1
checked on Sep 23, 2022

WEB OF SCIENCETM
Citations

1
checked on Sep 24, 2022

Page view(s)

10
checked on Feb 6, 2023

Google ScholarTM

Check

Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.