Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.11851/7123
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Güler, İnan | - |
dc.contributor.author | Übeyli, Derya Elif | - |
dc.date.accessioned | 2021-09-11T15:55:45Z | - |
dc.date.available | 2021-09-11T15:55:45Z | - |
dc.date.issued | 2005 | en_US |
dc.identifier.issn | 0941-0643 | - |
dc.identifier.uri | https://doi.org/10.1007/s00521-005-0473-0 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.11851/7123 | - |
dc.description.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. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Springer | en_US |
dc.relation.ispartof | Neural Computing & Applications | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Doppler ultrasound | en_US |
dc.subject | spectral analysis | en_US |
dc.subject | multilayer perceptron neural network | en_US |
dc.subject | quick propagation | en_US |
dc.subject | Uveitis disease | en_US |
dc.subject | ophthalmic artery | en_US |
dc.title | Neural Network Analysis of Ophthalmic Arterial Doppler Signals With Uveitis Disease | en_US |
dc.type | Article | en_US |
dc.department | Faculties, Faculty of Engineering, Department of Electrical and Electronics Engineering | en_US |
dc.department | Fakülteler, Mühendislik Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü | tr_TR |
dc.identifier.volume | 14 | en_US |
dc.identifier.issue | 4 | en_US |
dc.identifier.startpage | 353 | en_US |
dc.identifier.endpage | 360 | en_US |
dc.identifier.wos | WOS:000232985200010 | en_US |
dc.identifier.scopus | 2-s2.0-27744568576 | en_US |
dc.institutionauthor | Übeyli, Elif Derya | - |
dc.identifier.doi | 10.1007/s00521-005-0473-0 | - |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.identifier.scopusquality | Q4 | - |
item.openairetype | Article | - |
item.languageiso639-1 | en | - |
item.grantfulltext | none | - |
item.fulltext | No Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.cerifentitytype | Publications | - |
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 |
CORE Recommender
SCOPUSTM
Citations
1
checked on Dec 21, 2024
WEB OF SCIENCETM
Citations
1
checked on Nov 2, 2024
Page view(s)
88
checked on Dec 23, 2024
Google ScholarTM
Check
Altmetric
Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.