Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/5618
Title: Combining neural network models for automated diagnostic systems
Authors: Übeyli, Elif Derya
Keywords: Combined neural network model
Diagnostic accuracy
Discrete wavelet transform
Doppler signal
Internal carotid artery
Issue Date: 2006
Abstract: This paper illustrates the use of combined neural network (CNN) models to guide model selection for diagnosis of internal carotid arterial (ICA) disorders. The ICA Doppler signals were decomposed into time-frequency representations using discrete wavelet transform and statistical features were calculated to depict their distribution. The first level networks were implemented for the diagnosis of ICA disorders using the statistical features as inputs. To improve diagnostic accuracy, the second level network was trained using the outputs of the first level networks as input data. The CNN models achieved accuracy rates which were higher than that of the stand-alone neural network models. © 2006 Springer Science+Business Media, Inc.
URI: https://doi.org/10.1007/s10916-006-9034-z
https://hdl.handle.net/20.500.11851/5618
ISSN: 0148-5598
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

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