Diagnosing Knee Osteoarthritis Using Artificial Neural Networks and Deep Learning

Jean de Dieu Uwisengeyimana; Turgay Ibrikci

doi:doi:10.11648/j.bsi.20170203.11

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Diagnosing Knee Osteoarthritis Using Artificial Neural Networks and Deep Learning

Jean de Dieu Uwisengeyimana, Turgay Ibrikci

Published in Biomedical Statistics and Informatics (Volume 2, Issue 3)

Received: 24 January 2017 Accepted: 18 February 2017 Published: 29 March 2017

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Abstract

Among various medical diagnostic tests performed to identify osteoarthritis in the knee, most of them are invasive and expensive. Therefore, in this study, another methodology for diagnosing osteoarthritis in the knee in a more qwick, non-invasive and cheap manner was proposed. For that purpose, surface electromyography signals recorded from the four muscles surrounding the knee, the recording of the flexion degree in the knee and pattern recognition algorithms were used. The datasets of this experiment comprised 22 subjects among whom 11 subjects had normal knee and other 11 Subjects had an osteoarthritis-affected knee. The total sample size was 1, 048, 576 samples and were processed using segments of overlapping-windows of 5000 samples. Time-series features were then extracted from each segment and were used to train, test and validate 7 different learning classifiers and 7 variants of deep learning networks. In this study, the best performance measure of 99.5% was achieved by multilayer perceptron. Quadratic support vector machine and complex tree performed as well with accuracy of 99.4% and 98.3% respectively. In contrast, the use of deep learning networks which were investigated over a wide range of hidden size of the sparse autoencoders, showed accuracy of 86.6% with final softmax layer and accuracy of 91.3% by replacing the final softmax layer with k-nearest neighbour. By comparison, artificial neural networks outperformed deep learning networks and it is therefore concluded that the knee pathology can be diagnosed more effeciently and automatically using surface electromyography signals and artificial neural network algorithms.

Published in	Biomedical Statistics and Informatics (Volume 2, Issue 3)
DOI	10.11648/j.bsi.20170203.11
Page(s)	95-102
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Diagnosis, Electromyography, Deep Learning, Feature Extraction, Artificial Neural Network

References

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APA Style

Jean de Dieu Uwisengeyimana, Turgay Ibrikci. (2017). Diagnosing Knee Osteoarthritis Using Artificial Neural Networks and Deep Learning. Biomedical Statistics and Informatics, 2(3), 95-102. https://doi.org/10.11648/j.bsi.20170203.11

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ACS Style

Jean de Dieu Uwisengeyimana; Turgay Ibrikci. Diagnosing Knee Osteoarthritis Using Artificial Neural Networks and Deep Learning. Biomed. Stat. Inform. 2017, 2(3), 95-102. doi: 10.11648/j.bsi.20170203.11

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AMA Style

Jean de Dieu Uwisengeyimana, Turgay Ibrikci. Diagnosing Knee Osteoarthritis Using Artificial Neural Networks and Deep Learning. Biomed Stat Inform. 2017;2(3):95-102. doi: 10.11648/j.bsi.20170203.11

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@article{10.11648/j.bsi.20170203.11,
  author = {Jean de Dieu Uwisengeyimana and Turgay Ibrikci},
  title = {Diagnosing Knee Osteoarthritis Using Artificial Neural Networks and Deep Learning},
  journal = {Biomedical Statistics and Informatics},
  volume = {2},
  number = {3},
  pages = {95-102},
  doi = {10.11648/j.bsi.20170203.11},
  url = {https://doi.org/10.11648/j.bsi.20170203.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.bsi.20170203.11},
  abstract = {Among various medical diagnostic tests performed to identify osteoarthritis in the knee, most of them are invasive and expensive. Therefore, in this study, another methodology for diagnosing osteoarthritis in the knee in a more qwick, non-invasive and cheap manner was proposed. For that purpose, surface electromyography signals recorded from the four muscles surrounding the knee, the recording of the flexion degree in the knee and pattern recognition algorithms were used. The datasets of this experiment comprised 22 subjects among whom 11 subjects had normal knee and other 11 Subjects had an osteoarthritis-affected knee. The total sample size was 1, 048, 576 samples and were processed using segments of overlapping-windows of 5000 samples. Time-series features were then extracted from each segment and were used to train, test and validate 7 different learning classifiers and 7 variants of deep learning networks. In this study, the best performance measure of 99.5% was achieved by multilayer perceptron. Quadratic support vector machine and complex tree performed as well with accuracy of 99.4% and 98.3% respectively. In contrast, the use of deep learning networks which were investigated over a wide range of hidden size of the sparse autoencoders, showed accuracy of 86.6% with final softmax layer and accuracy of 91.3% by replacing the final softmax layer with k-nearest neighbour. By comparison, artificial neural networks outperformed deep learning networks and it is therefore concluded that the knee pathology can be diagnosed more effeciently and automatically using surface electromyography signals and artificial neural network algorithms.},
 year = {2017}
}

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TY  - JOUR
T1  - Diagnosing Knee Osteoarthritis Using Artificial Neural Networks and Deep Learning
AU  - Jean de Dieu Uwisengeyimana
AU  - Turgay Ibrikci
Y1  - 2017/03/29
PY  - 2017
N1  - https://doi.org/10.11648/j.bsi.20170203.11
DO  - 10.11648/j.bsi.20170203.11
T2  - Biomedical Statistics and Informatics
JF  - Biomedical Statistics and Informatics
JO  - Biomedical Statistics and Informatics
SP  - 95
EP  - 102
PB  - Science Publishing Group
SN  - 2578-8728
UR  - https://doi.org/10.11648/j.bsi.20170203.11
AB  - Among various medical diagnostic tests performed to identify osteoarthritis in the knee, most of them are invasive and expensive. Therefore, in this study, another methodology for diagnosing osteoarthritis in the knee in a more qwick, non-invasive and cheap manner was proposed. For that purpose, surface electromyography signals recorded from the four muscles surrounding the knee, the recording of the flexion degree in the knee and pattern recognition algorithms were used. The datasets of this experiment comprised 22 subjects among whom 11 subjects had normal knee and other 11 Subjects had an osteoarthritis-affected knee. The total sample size was 1, 048, 576 samples and were processed using segments of overlapping-windows of 5000 samples. Time-series features were then extracted from each segment and were used to train, test and validate 7 different learning classifiers and 7 variants of deep learning networks. In this study, the best performance measure of 99.5% was achieved by multilayer perceptron. Quadratic support vector machine and complex tree performed as well with accuracy of 99.4% and 98.3% respectively. In contrast, the use of deep learning networks which were investigated over a wide range of hidden size of the sparse autoencoders, showed accuracy of 86.6% with final softmax layer and accuracy of 91.3% by replacing the final softmax layer with k-nearest neighbour. By comparison, artificial neural networks outperformed deep learning networks and it is therefore concluded that the knee pathology can be diagnosed more effeciently and automatically using surface electromyography signals and artificial neural network algorithms.
VL  - 2
IS  - 3
ER  -

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Author Information

Jean de Dieu Uwisengeyimana

Department of Electrical and Electronics Engineering, Cukurova University, Adana, Turkey
Turgay Ibrikci

Department of Electrical and Electronics Engineering, Cukurova University, Adana, Turkey

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APA Style

Jean de Dieu Uwisengeyimana, Turgay Ibrikci. (2017). Diagnosing Knee Osteoarthritis Using Artificial Neural Networks and Deep Learning. Biomedical Statistics and Informatics, 2(3), 95-102. https://doi.org/10.11648/j.bsi.20170203.11

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ACS Style

Jean de Dieu Uwisengeyimana; Turgay Ibrikci. Diagnosing Knee Osteoarthritis Using Artificial Neural Networks and Deep Learning. Biomed. Stat. Inform. 2017, 2(3), 95-102. doi: 10.11648/j.bsi.20170203.11

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AMA Style

Jean de Dieu Uwisengeyimana, Turgay Ibrikci. Diagnosing Knee Osteoarthritis Using Artificial Neural Networks and Deep Learning. Biomed Stat Inform. 2017;2(3):95-102. doi: 10.11648/j.bsi.20170203.11

Copy | Download

@article{10.11648/j.bsi.20170203.11,
  author = {Jean de Dieu Uwisengeyimana and Turgay Ibrikci},
  title = {Diagnosing Knee Osteoarthritis Using Artificial Neural Networks and Deep Learning},
  journal = {Biomedical Statistics and Informatics},
  volume = {2},
  number = {3},
  pages = {95-102},
  doi = {10.11648/j.bsi.20170203.11},
  url = {https://doi.org/10.11648/j.bsi.20170203.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.bsi.20170203.11},
  abstract = {Among various medical diagnostic tests performed to identify osteoarthritis in the knee, most of them are invasive and expensive. Therefore, in this study, another methodology for diagnosing osteoarthritis in the knee in a more qwick, non-invasive and cheap manner was proposed. For that purpose, surface electromyography signals recorded from the four muscles surrounding the knee, the recording of the flexion degree in the knee and pattern recognition algorithms were used. The datasets of this experiment comprised 22 subjects among whom 11 subjects had normal knee and other 11 Subjects had an osteoarthritis-affected knee. The total sample size was 1, 048, 576 samples and were processed using segments of overlapping-windows of 5000 samples. Time-series features were then extracted from each segment and were used to train, test and validate 7 different learning classifiers and 7 variants of deep learning networks. In this study, the best performance measure of 99.5% was achieved by multilayer perceptron. Quadratic support vector machine and complex tree performed as well with accuracy of 99.4% and 98.3% respectively. In contrast, the use of deep learning networks which were investigated over a wide range of hidden size of the sparse autoencoders, showed accuracy of 86.6% with final softmax layer and accuracy of 91.3% by replacing the final softmax layer with k-nearest neighbour. By comparison, artificial neural networks outperformed deep learning networks and it is therefore concluded that the knee pathology can be diagnosed more effeciently and automatically using surface electromyography signals and artificial neural network algorithms.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Diagnosing Knee Osteoarthritis Using Artificial Neural Networks and Deep Learning
AU  - Jean de Dieu Uwisengeyimana
AU  - Turgay Ibrikci
Y1  - 2017/03/29
PY  - 2017
N1  - https://doi.org/10.11648/j.bsi.20170203.11
DO  - 10.11648/j.bsi.20170203.11
T2  - Biomedical Statistics and Informatics
JF  - Biomedical Statistics and Informatics
JO  - Biomedical Statistics and Informatics
SP  - 95
EP  - 102
PB  - Science Publishing Group
SN  - 2578-8728
UR  - https://doi.org/10.11648/j.bsi.20170203.11
AB  - Among various medical diagnostic tests performed to identify osteoarthritis in the knee, most of them are invasive and expensive. Therefore, in this study, another methodology for diagnosing osteoarthritis in the knee in a more qwick, non-invasive and cheap manner was proposed. For that purpose, surface electromyography signals recorded from the four muscles surrounding the knee, the recording of the flexion degree in the knee and pattern recognition algorithms were used. The datasets of this experiment comprised 22 subjects among whom 11 subjects had normal knee and other 11 Subjects had an osteoarthritis-affected knee. The total sample size was 1, 048, 576 samples and were processed using segments of overlapping-windows of 5000 samples. Time-series features were then extracted from each segment and were used to train, test and validate 7 different learning classifiers and 7 variants of deep learning networks. In this study, the best performance measure of 99.5% was achieved by multilayer perceptron. Quadratic support vector machine and complex tree performed as well with accuracy of 99.4% and 98.3% respectively. In contrast, the use of deep learning networks which were investigated over a wide range of hidden size of the sparse autoencoders, showed accuracy of 86.6% with final softmax layer and accuracy of 91.3% by replacing the final softmax layer with k-nearest neighbour. By comparison, artificial neural networks outperformed deep learning networks and it is therefore concluded that the knee pathology can be diagnosed more effeciently and automatically using surface electromyography signals and artificial neural network algorithms.
VL  - 2
IS  - 3
ER  -

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