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Identifying Severity Grading of Knee Osteoarthritis from X-ray Images Using an Efficient Mixture of Deep Learning and
Sozan Mohammed Ahmed1, Ramadhan J Mstafa1,2
1Department of Computer Science, Faculty of Science, University of Zakho, Duhok 42002, Iraq.
Diagnostics (Basel, Switzerland)
|December 23, 2022
Summary
This study introduces a new method using AI and X-ray images for early knee osteoarthritis (OA) prediction. Binary classification achieved 90.8% accuracy, aiding timely treatment and improving patient quality of life.
Area of Science:
- Medical Imaging and Artificial Intelligence
- Orthopedics and Rheumatology
Background:
- Knee osteoarthritis (OA) significantly impacts lifestyle, especially in older adults, necessitating efficient diagnostic tools.
- Current diagnostic methods for knee OA can be time-consuming and costly, highlighting the need for rapid, accurate, and low-cost computer-based solutions.
- Early prediction of knee OA is crucial for timely intervention and disease management.
Purpose of the Study:
- To develop and evaluate novel computer-based methods for the early diagnosis and severity classification of knee osteoarthritis using X-ray images.
- To investigate the impact of different class-based classification strategies (binary and multiclass) on diagnostic performance.
- To provide physicians with versatile deployment options for knee OA assessment.
Main Methods:
- Two frameworks were developed utilizing pre-trained Convolutional Neural Networks (CNNs) for feature extraction and transfer learning (TL) for model fine-tuning.
- Framework 1 employed CNN feature extraction, Principal Component Analysis (PCA) for dimensionality reduction, and Support Vector Machine (SVM) for classification across five classes.
- Framework 2 adapted TL to fine-tune CNNs for two, three, and four class-based models, integrating traditional machine learning classifiers.
Main Results:
- The proposed models demonstrated improved classification accuracy for knee OA in both multiclass and binary scenarios compared to existing state-of-the-art methods.
- Experimental analysis indicated that models with fewer multiclass labels achieved better performance, with binary classification yielding the highest accuracy at 90.8%.
- The developed approach effectively contributes to the early classification of knee OA, potentially reducing disease progression.
Conclusions:
- The AI-driven approach using X-ray images offers a promising solution for efficient and accurate knee OA diagnosis and classification.
- Binary classification models proved most effective, achieving high accuracy and supporting early disease detection.
- The study's findings can aid in reducing knee OA progression and enhancing the quality of life for affected individuals.
