Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/7374
Title: Recurrent Neural Networks for Diagnosis of Carpal Tunnel Syndrome Using Electrophysiologic Findings
Authors: İlbay, Konuralp
Übeyli, Elif Derya
İlbay, Gül
Budak, Faik
Keywords: Carpal tunnel syndrome
Median motor latency
Median sensory latency
Clasification accuracy
Recurrent neural network
Issue Date: 2010
Publisher: Springer
Abstract: This paper presents the use of recurrent neural networks (RNNs) for diagnosis of carpal tunnel syndrome (CTS) (normal, right CTS, left CTS, bilateral CTS). The RNN is trained with the Levenberg-Marquardt algorithm. The RNN is trained on the features of CTS (right median motor latency, left median motor latency, right median sensory latency, left median sensory latency). The multilayer perceptron neural network (MLPNN) is also implemented for comparison the performance of the classifiers on the same diagnosis problem. The total classification accuracy of the RNN is significantly high (94.80%). The obtained results confirmed the validity of the RNNs to help in clinical decision-making.
URI: https://doi.org/10.1007/s10916-009-9277-6
https://hdl.handle.net/20.500.11851/7374
ISSN: 0148-5598
1573-689X
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
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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