Automated Detection of Posterior Tibial Slope on X-Ray Images Using VGG19
DOI:
https://doi.org/10.6000/1929-6029.2025.14.63Keywords:
Convolutional Neural Networks (CNNs), Deep learning, Machine learning, VGG-19 architecture, Image analysis, Pattern recognitionAbstract
Elderly and overweight individuals are particularly vulnerable to developing muscle weakness and joint pain as a result of osteoarthritis (OA). This degenerative joint condition often affects the ligaments and primarily damages the cartilage. Healthy cartilage, being smooth and elastic, enables bones to glide effortlessly over one another, providing stability and preventing friction between bone surfaces. When this protective tissue deteriorates partially or completely, it results in painful stiffness and discomfort caused by direct bone contact. The diagnosis of osteoarthritis typically involves a combination of clinical assessment and diagnostic imaging techniques such as X-rays or MRI scans. The present study focuses on utilising advanced image-based feature extraction methods for the identification and classification of knee osteoarthritis. This approach aims to enhance diagnostic accuracy by improving the differentiation of structural changes observed in medical images.
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