Predicting Knee Osteoarthritis Grades using Deep Learning - A Extensive Examination

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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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  -