Osteoarthritis grading: a synthesized magnetic resonance images technique / Qiu Ruiyun ... [et al.]

-, Qiu Ruiyun and Abdul Rahim, Siti Khatijah Nor and Jamil, Nursuriati and Hamzah, Raseeda and -, Fu Xiaoling (2024) Osteoarthritis grading: a synthesized magnetic resonance images technique / Qiu Ruiyun ... [et al.]. Malaysian Journal of Computing (MJoC), 9 (2): 14. pp. 1944-1954. ISSN 2600-8238

Abstract

Osteoarthritis (OA) in the knee is a major cause of decreased activity and physical limitations among older people. Identifying and treating knee osteoarthritis in its early stages can help patients delay the progression of the condition. Currently, early detection of knee osteoarthritis involves the use of X-ray images and assessment using the Kellgren-Lawrence (KL) grading system. Doctors' evaluations can be subjective and may differ among different doctors. Similar to a computer systems analyst, the automatic knee OA grading and diagnosis can be a valuable tool for doctors, enabling them to streamline their workload and provide more efficient care. An innovative network named OA_GAN_ViT has been developed to autonomously detect knee OA. The network is a ViT architecture consisting of two branches: one branch utilizes the synthesized MR image derived from X-ray images for data processing before classification operations via the GAN network, while the other branch employs a histogram-equalized X-ray image. The OA_GAN_ViT network demonstrated superior performance in terms of accuracy and MAE compared to well-known neural networks such as ResNet, DenseNet, VGG, Inception, and ViT. It achieved an impressive accuracy of 79.2 and an MAE of 0.492, highlighting its effectiveness.

Metadata

Item Type: Article
Creators:
Creators
Email / ID Num.
-, Qiu Ruiyun
qryun1228@qq.com
Abdul Rahim, Siti Khatijah Nor
sitik781@uitm.edu.my
Jamil, Nursuriati
liza@uitm.edu.my
Hamzah, Raseeda
raseeda@tmsk.uitm.edu.my
-, Fu Xiaoling
fxl982@sina.com
Subjects: Q Science > Q Science (General) > Machine learning
R Medicine > RC Internal Medicine > Chronic diseases
Divisions: Universiti Teknologi MARA, Shah Alam > College of Computing, Informatics and Mathematics
Journal or Publication Title: Malaysian Journal of Computing (MJoC)
UiTM Journal Collections: Listed > Malaysian Journal of Computing (MJoC)
ISSN: 2600-8238
Volume: 9
Number: 2
Page Range: pp. 1944-1954
Keywords: Deep Learning, Multimodal Synthesis, OA Grading, Pre-process
Date: October 2024
URI: https://ir.uitm.edu.my/id/eprint/105191
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