Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6397
Title: Combined neural networks for diagnosis of erythemato-squamous diseases
Authors: Übeyli, Elif Derya
Keywords: Combined neural network (CNN)
Erythemato-squamous diseases
Diagnostic accuracy
Issue Date: 2009
Publisher: Pergamon-Elsevier Science Ltd
Abstract: This paper illustrates the use of combined neural networks (CNNs) model to guide model selection for diagnosis of the erythemato-squamous diseases. The multilayer perceptron neural networks (MLPNNs) were also tested and benchmarked for their performance on the diagnosis of the erythemato-squamous diseases. The domain contained records of patients with known diagnosis. Given a training set of such records, the classifiers learned how to differentiate a new case in the domain. The first level networks were used to detect the six erythemato-squamous diseases when 34 features defining six disease indications were used as inputs. To improve diagnostic accuracy, the second level networks were trained using the outputs of the first level networks as input data. The CNN model achieved accuracy rates which were higher than that of the stand-alone neural network model (MLPNN). (C) 2008 Elsevier Ltd. All rights reserved.
URI: https://doi.org/10.1016/j.eswa.2008.06.002
https://hdl.handle.net/20.500.11851/6397
ISSN: 0957-4174
1873-6793
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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