Automatic Knee Osteoarthritis Severity Grading using Deep Neural Networks: Comparative Analysis of Network Architectures and Optimization Functions


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Authors

  • Ahmet Ezgi Izmir Katip Celebi University
  • Aytuğ Onan Izmir Katip Celebi University

DOI:

https://doi.org/10.59287/icaens.992

Keywords:

Knee Osteoarthritis, Deep Neural Networks, Severity Grading, Network Architectures, Optimization Functions

Abstract

Knee osteoarthritis (OA) is a prevalent degenerative joint disease that requires accurate assessment of its severity for effective treatment planning. In this study, we propose an automatic knee OA severity-grading system based on deep neural networks. Specifically, we explore various network architectures, including VGG-16, VGG-19, ResNet-101, EfficientNet-B7, and EfficientNet-B6, along with different optimization functions such as SGD, ADAM, Nadam, AdamW, and AdaDelta. Furthermore, we investigate two loss functions, namely, the novel ordinal loss and the cross-entropy loss. The proposed system is evaluated on a carefully curated dataset, and comprehensive experimental settings are employed to ensure reliable results. Our findings indicate that the combination of the EfficientNet-B7 network with the Nadam optimizer yields the best performance, achieving an accuracy of 70.1% in knee OA severity grading. These results demonstrate the potential of deep neural networks in automating the grading process, offering a valuable tool for clinicians and researchers in the field of knee osteoarthritis management.

Author Biographies

Ahmet Ezgi, Izmir Katip Celebi University

Department of Software Engineering, Turkey

Aytuğ Onan, Izmir Katip Celebi University

Department of Computer Engineering,Turkey

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Published

2023-07-20

How to Cite

Ezgi, A., & Onan, A. (2023). Automatic Knee Osteoarthritis Severity Grading using Deep Neural Networks: Comparative Analysis of Network Architectures and Optimization Functions. International Conference on Applied Engineering and Natural Sciences, 1(1), 197–203. https://doi.org/10.59287/icaens.992