research-article

Deep Neural Networks for Automatic Classification of Knee Osteoarthritis Severity Based on X-ray Images

Authors:

Rima Tri Wahyuningrum,

Gijbertus Jacob VerkerkeAuthors Info & Claims

ICIT '20: Proceedings of the 2020 8th International Conference on Information Technology: IoT and Smart City

Pages 110 - 114

https://doi.org/10.1145/3446999.3447020

Published: 09 April 2021 Publication History

Abstract

Knee Osteoarthritis (KOA) is a type of chronic disease that commonly occurs in older, obese citizens and those with a sedentary lifestyle. This disease causes damage to knee cartilage and causes pain so that the patient's activity is reduced. Radiologists classify the KOA severity based on Joint Space Narrowing (JSN) and the presence or absence of osteophytes into five stages from healthy knee (stage 0) to the worst damage (stage 4). We developed a methodology that aims to accelerate the classification of KOA severity based on information obtained from X-ray images and to reduce the subjectivity of radiologists. This paper describes an automated KOA diagnostic model using hyper-parameter Deep Convolutional Neural Networks (DCNN). Based on our experimental result, it shows the accuracy of the proposed method outperforms other KOA severity classification algorithms, which also discussed deep learning, namely 77.24%. This value is the average result of the accuracy of each fold from each stage of the KOA severity level where we use three-folds cross validation as a method of evaluating system performance. Thus computationally, this method is efficient in automatic diagnosis and has the potential to be a clinician application aid to specify the KOA severity.

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Cited By

Rani SMemoria MChoudhury TSar A(2024)A Comprehensive Review of Machine Learning’s Role within KOAEAI Endorsed Transactions on Internet of Things10.4108/eetiot.532910Online publication date: 7-Mar-2024
https://doi.org/10.4108/eetiot.5329
Touahema SZaimi IZrira NNgote MDoulhousne HAouial M(2024)MedKnee: A New Deep Learning-Based Software for Automated Prediction of Radiographic Knee OsteoarthritisDiagnostics10.3390/diagnostics1410099314:10(993)Online publication date: 10-May-2024
https://doi.org/10.3390/diagnostics14100993
Touahema SZaimi IZrira NNgote M(2024)How Can Artificial Intelligence Identify Knee Osteoarthritis from Radiographic Images with Satisfactory Accuracy?: A Literature Review for 2018–2024Applied Sciences10.3390/app1414633314:14(6333)Online publication date: 20-Jul-2024
https://doi.org/10.3390/app14146333
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cover image ACM Other conferences

ICIT '20: Proceedings of the 2020 8th International Conference on Information Technology: IoT and Smart City

December 2020

266 pages

ISBN:9781450388559

DOI:10.1145/3446999

Copyright © 2020 ACM.

© 2020 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 April 2021

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This work has been supported by International Collaborative Research, Universitas Trunojoyo Madura in 2020

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ICIT 2020

ICIT 2020: IoT and Smart City

December 25 - 27, 2020

Xi'an, China

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Cited By

Rani SMemoria MChoudhury TSar A(2024)A Comprehensive Review of Machine Learning’s Role within KOAEAI Endorsed Transactions on Internet of Things10.4108/eetiot.532910Online publication date: 7-Mar-2024
https://doi.org/10.4108/eetiot.5329
Touahema SZaimi IZrira NNgote MDoulhousne HAouial M(2024)MedKnee: A New Deep Learning-Based Software for Automated Prediction of Radiographic Knee OsteoarthritisDiagnostics10.3390/diagnostics1410099314:10(993)Online publication date: 10-May-2024
https://doi.org/10.3390/diagnostics14100993
Touahema SZaimi IZrira NNgote M(2024)How Can Artificial Intelligence Identify Knee Osteoarthritis from Radiographic Images with Satisfactory Accuracy?: A Literature Review for 2018–2024Applied Sciences10.3390/app1414633314:14(6333)Online publication date: 20-Jul-2024
https://doi.org/10.3390/app14146333
Alsaidi FMoria K(2023)Flatfeet Severity-Level Detection Based on Alignment MeasuringSensors10.3390/s2319821923:19(8219)Online publication date: 2-Oct-2023
https://doi.org/10.3390/s23198219
Khalid ASenan EAl-Wagih KAli Al-Azzam MAlkhraisha Z(2023)Hybrid Techniques of X-ray Analysis to Predict Knee Osteoarthritis Grades Based on Fusion Features of CNN and HandcraftedDiagnostics10.3390/diagnostics1309160913:9(1609)Online publication date: 2-May-2023
https://doi.org/10.3390/diagnostics13091609
Khessiba SBlaiech AAbdallah AGrassa RManzanera ABedoui M(2023)Improving Knee Osteoarthritis Classification with Markerless Pose Estimation and STGCN Model2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP)10.1109/MMSP59012.2023.10337688(1-7)Online publication date: 27-Sep-2023
https://doi.org/10.1109/MMSP59012.2023.10337688
Shourie PAnand VGupta S(2023)A Sophisticated Method for X-Ray Image-Based Knee Osteoarthritis Diagnosis Utilising Moblienetv3 Large2023 Global Conference on Information Technologies and Communications (GCITC)10.1109/GCITC60406.2023.10426267(1-5)Online publication date: 1-Dec-2023
https://doi.org/10.1109/GCITC60406.2023.10426267
Aladhadh SMahum R(2023)Knee Osteoarthritis Detection Using an Improved CenterNet With Pixel-Wise Voting SchemeIEEE Access10.1109/ACCESS.2023.324750211(22283-22296)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3247502
M GGoswami A(2023)Knee osteoarthritis classification using social wolf swarm-based deep convolutional neural networkComputer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization10.1080/21681163.2023.220326611:5(1947-1959)Online publication date: 20-Apr-2023
https://doi.org/10.1080/21681163.2023.2203266
Supatman Yuniarno EPurnomo M(2022)Classification Anterior and Posterior of Knee Osteoarthritis X-Ray Images Grade KL-2 Using Deep Learning with Random Brightness Augmentation2022 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM)10.1109/CENIM56801.2022.10037483(1-5)Online publication date: 22-Nov-2022
https://doi.org/10.1109/CENIM56801.2022.10037483

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