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 |
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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