Deep Learning-based Automated Knee Joint Localization in Radiographic Images Using Faster R-CNN

Area of Science:

• Medical Imaging Analysis
• Artificial Intelligence in Healthcare
• Deep Learning Applications

Background:

• Osteoarthritis poses risks to knee joints, causing pain and functional impairment.
• Traditional knee X-ray evaluations (Kellgren-Lawrence grading) are inefficient, subjective, and time-consuming.
• Current methods are labor-intensive, especially in high-volume hospital settings.

Purpose of the Study:

• To present a deep learning-based approach for detecting knee joint regions in medical images.
• To develop a more efficient and automated method for knee joint analysis.
• To overcome limitations of traditional diagnostic techniques for knee disorders.

Main Methods:

• Utilized the Faster R-CNN deep learning model, comprising a region proposal network (RPN) and Fast R-CNN.
• RPN generated potential knee joint region proposals.
• Fast R-CNN categorized and extracted features; model trained on knee joint images and evaluated using accuracy, precision, recall, F1-score, and mean IoU.

Main Results:

• The deep learning model demonstrated high accuracy in detecting knee joint regions.
• Achieved a mean IoU of 94.5, signifying strong overlap between predicted and actual regions.
• Highlighted the potential of AI in automating medical image analysis for knee disorders.

Conclusions:

• Emphasized the significance of advanced technologies like deep learning in medical imaging.
• Developing efficient, accurate methods for knee joint region identification can enhance patient outcomes.
• The proposed deep learning approach shows promise for advancing medical image analysis and diagnostic capabilities for knee joint disorders.