Improving YOLO Efficient for Knee Osteoarthritis Detection Using Minkowski Distance

Authors

  • Marwah Fadhil Najim
  • Atheer Alrammahi Department of Medical Intelligent Systems, University of Al-Qadisiyah, Iraq.
  • Ahmed Mohsen Mahdi Department of Computer Science, University of Al-Qadisiyah, Iraq

DOI:

https://doi.org/10.29304/jqcsm.2025.17.32376

Keywords:

Knee Osteoarthritis (KOA), YOLO, Deep Learning, KL Grading

Abstract

This study presents an enhanced YOLO-based deep learning model for automatic detection of knee osteoarthritis (KOA) from X-ray images. The integration of Minkowski distance into the loss function improves the model’s sensitivity to spatial variations and noise. Extensive preprocessing and a lightweight YOLO architecture ensure real-time performance with high accuracy. Experimental results demonstrate superior detection rates compared to traditional methods, especially in identifying early-stage KOA.

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References

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Published

2025-09-30

How to Cite

Fadhil Najim, M., Alrammahi, A., & Mohsen Mahdi, A. (2025). Improving YOLO Efficient for Knee Osteoarthritis Detection Using Minkowski Distance. Journal of Al-Qadisiyah for Computer Science and Mathematics, 17(3), Comp. 19–33. https://doi.org/10.29304/jqcsm.2025.17.32376

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Section

Computer Articles