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Related Experiment Video

Updated: Jul 30, 2025

Software-Assisted Quantitative Measurement of Osteoarthritic Subchondral Bone Thickness
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Knee Osteoarthritis Detection and Severity Classification Using Residual Neural Networks on Preprocessed X-ray

Abdul Sami Mohammed1, Ahmed Abul Hasanaath2, Ghazanfar Latif2

  • 1Computer Engineering Department, Prince Mohammad Bin Fahd University, Al-Khobar 31952, Saudi Arabia.

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Summary

This study introduces deep neural network (DNN) models for automated knee osteoarthritis (KOA) diagnosis from X-rays. ResNet101 achieved the highest accuracy, improving KOA classification performance.

Keywords:
X-ray imagesknee osteoarthritisresidual neural networksseverity classification

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Machine Learning

Background:

  • Knee osteoarthritis (KOA) is a common, challenging condition in the elderly.
  • Manual KOA diagnosis via X-ray using the Kellgren-Lawrence (KL) system is time-consuming and error-prone.
  • Deep neural networks (DNNs) offer potential for automated, accurate KOA detection and grading.

Purpose of the Study:

  • To evaluate the efficacy of six pretrained DNN models for automated KOA diagnosis.
  • To perform binary (presence/absence) and three-class (severity) KOA classification.
  • To compare DNN model performance across different datasets.

Main Methods:

  • Application of six pretrained DNN models: VGG16, VGG19, ResNet101, MobileNetV2, InceptionResNetV2, and DenseNet121.
  • Utilized images from the Osteoarthritis Initiative (OAI) dataset.
  • Conducted binary and three-class classification tasks on three distinct datasets.

Main Results:

  • ResNet101 achieved maximum classification accuracies of 69% (Dataset I), 83% (Dataset II), and 89% (Dataset III).
  • The study demonstrated improved performance compared to existing literature.
  • DNN models showed effectiveness in both KOA presence detection and severity grading.

Conclusions:

  • Pretrained DNN models, particularly ResNet101, show significant promise for accurate and efficient automated knee osteoarthritis diagnosis.
  • The findings suggest a viable AI-driven approach to supplement traditional diagnostic methods.
  • Further research can explore larger datasets and advanced model architectures for enhanced KOA classification.