Osteoarthritis Detection Algorithm Using CNN and Image Processing Techniques | ||
| Journal of Communication Sciences and Information Technology | ||
| Article 1, Volume 3, Issue 1, May 2024, Pages 1-8 PDF (363.85 K) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/jcsit.2024.288643.1005 | ||
| Author | ||
| Hanafy Ali* | ||
| Computers and Systems engineering Depart., Faculty of Engineering | ||
| Abstract | ||
| Osteoarthritis is a prevalent chronic disease affecting various joints, primarily the fingers, thumbs, spine, hips, knees, and big toes, with secondary occurrences linked to pre-existing joint anomalies. Although more common among older individuals, OA can develop in adults of any age, characterized by degenerative changes in joints. Automatic segmentation and interpretation of joint MRI scans are thus necessary to enhance clinical outcomes and bone calculation precision. The advent of deep learning technologies in medical systems has facilitated such transition, enabling efficient processing of large data volumes with improved accuracy. Deep learning methods, particularly Convolutional Neural Networks (CNNs), have proven effectiveness in automating MRI scan segmentation. The paper provides an overview of various deep learning and image processing techniques employed for automatic segmentation and interpretation of MRI scans, facilitating disease diagnosis based on image data. State-of-the-art analyses, particularly focusing on CNNs and Image Recognition, are discussed, followed by a comparative evaluation of the proposed model against other techniques based on performance metrics. The proposed detection algorithm demonstrates promising results, achieving predictive accuracies exceeding 90% across all sugested models. Particularly noteworthy is the performance of the proposed pre-trained VGG-16 model with edge detection, which attained a training accuracy of 100% and a testing accuracy of 98.2%. This highlights the efficacy of deep learning approaches in enhancing diagnostic accuracy and efficiency in knee OA detection. | ||
| Keywords | ||
| Osteoarthritis; CNN; Classification; feature extraction; knee osteoarthritis detection | ||
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