Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)

Predicting Knee Osteoarthritis Grades using Deep Learning - A Extensive Examination

Authors
V. Vijaya Kishore1, *, G. Hema Padmini2, I. Sudharshan2, K. Tejaswini2, M. Roopa2, Rambabu Inaganti3
1Prof, Dept of ECE, Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College), Tirupati, India
2Dept of ECE, Erstwhile Sree Vidyanikethan Engineering College, Tirupati, India
3Smith & Nephew, Pittsburgh, PA, 15108, USA
*Corresponding author. Email: kishiee@rediffmail.com
Corresponding Author
V. Vijaya Kishore
Available Online 30 July 2024.
DOI
10.2991/978-94-6463-471-6_71How to use a DOI?
Keywords
Deep Learning; X-rays; Adaptive neural network; Gradient descent optimization; Leaky ReLU
Abstract

A major global health concern is knee osteoarthritis, which is frequently identified by conventional radiographic grading schemes like the Kellgren-Lawrence scale. Reliance on X-ray pictures, however, may cause a delayed diagnosis. Convolutional neural networks (CNNs), in particular, are deep learning techniques that have been the subject of recent research aimed at improving diagnostic efficiency and accuracy. Eight CNN-based adaptive neural network models were examined for the diagnosis of knee osteoarthritis. These models were trained and verified using a large dataset of knee X-rays, and then their ability to classify the severity of osteoarthritis in the knee was thoroughly examined. According to our findings, the best-performing model outperformed the others, with an astounding accuracy of 98.73%. This study highlights how deep learning models, in particular CNNs, can increase knee osteoarthritis diagnosis accuracy and speed.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
Series
Advances in Computer Science Research
Publication Date
30 July 2024
ISBN
978-94-6463-471-6
ISSN
2352-538X
DOI
10.2991/978-94-6463-471-6_71How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

TY  - CONF
AU  - V. Vijaya Kishore
AU  - G. Hema Padmini
AU  - I. Sudharshan
AU  - K. Tejaswini
AU  - M. Roopa
AU  - Rambabu Inaganti
PY  - 2024
DA  - 2024/07/30
TI  - Predicting Knee Osteoarthritis Grades using Deep Learning - A Extensive Examination
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 734
EP  - 744
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_71
DO  - 10.2991/978-94-6463-471-6_71
ID  - Kishore2024
ER  -