Deep Learning-Based Framework for Automated Classification of Knee Osteoarthritis Severity and Detection of Joint Space Width in X-Ray Imaging

DOI

10.2991/978-94-6463-718-2_34How to use a DOI?

Keywords

Knee osteoarthritis severity classification; Deep Learning; YOLOv8; VGG16; KL grading

Abstract

Knee osteoarthritis (OA) is a widespread degenerative condition impacting millions, predominantly older adults, and frequently resulting in chronic pain and a compromised quality of life. Timely and precise diagnosis is crucial for effective disease management, mitigating its progression, and enabling the implementation of targeted therapeutic interventions. In this paper we present a novel framework that employs deep learning for analysis of digital X-ray images to identify and classify knee OA. The method solves two problems, the measurement of Knee Joint Space Width (JSW) an essential marker for OA, and determining the severity of an OA knee using the conventional Kellgren-Lawrence (KL) grading method. To do so, we utilize the YOLOv8 model, which is known for its rapid and accurate detection for locating and segmenting the JSW region from X-ray images. Upon detection of the JSW, a VGG16 neural network augmented through transfer learned is applied. To classify the OA severity according to KL grades the system automatically delivers useful diagnostic information to clinicians to enhance the meaningfulness of OA progression assessments and to facilitate earlier and more successful intervention. Our framework harnesses the power of advanced deep learning techniques, to support improved patient outcomes, and increase diagnostic precision.

Copyright

© 2025 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  - Logeshwari Alavanthar
AU  - Jayashree Stalin
AU  - K. Jasmine Mystica
PY  - 2025
DA  - 2025/05/23
TI  - Deep Learning-Based Framework for Automated Classification of Knee Osteoarthritis Severity and Detection of Joint Space Width in X-Ray Imaging
BT  - Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
PB  - Atlantis Press
SP  - 391
EP  - 400
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-718-2_34
DO  - 10.2991/978-94-6463-718-2_34
ID  - Alavanthar2025
ER  -