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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Ruiyun, Qiu
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Computer Science & IT Engineering

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DualVitOA: A dual vision transformer-based model for osteoarthritis grading using x-ray images Ruiyun, Qiu; Abdul Rahim, Siti Khatijah Nor; Jamil, Nursuriati; Hamzah, Raseeda
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp925-932

Abstract

Knee osteoarthritis (OA) is a primary factor contributing to reduced activity and physical impairment in older individuals. Early identification and treatment of knee OA can assist patients in delaying the advancement of the condition. Currently, knee OA is detected early using X-ray images and assessed based on the Kellgren-Lawrence (KL) grading system. Doctors' assessments are subjective and can vary among different doctors. The automatic knee OA grading and diagnosis can assist doctors and help doctors reduce their workload. A new novel network called dual-vision transformer (ViT) OA is proposed to automatically diagnose knee OA. The network utilizes pre-processing technologies to process the data before doing classification operations using the Dual-ViT network. The suggested network outperformed neural networks like ResNet, DenseNet, visual geometry group (VGG), inception, and ViT in terms of accuracy and mean absolute error (MAE), and achieved an accuracy of 78.4 and MAE of 0.471, demonstrating its effectiveness.
Co-Authors Abdul Rahim, Siti Khatijah Nor Hamzah, Raseeda Jamil, Nursuriati