Dikshant Pakhale,
Shilpa Jahagirdar,
Harsh Mahajan,
Sahil Raut,
- Student Department of Electronic and Telecomminucation, Smt. Kashibai Navale College of Engineering, Pune India
- Assistant Professor Department of Electronic and Telecomminucation, Smt. Kashibai Navale College of Engineering, Pune India
- Student Department of Electronic and Telecomminucation, Smt. Kashibai Navale College of Engineering, Pune India
- Student Department of Electronic and Telecomminucation, Smt. Kashibai Navale College of Engineering, Pune India
Abstract
Osteoarthritis is the most prevalent form of arthritis affecting the knee. It is a degenerative joint disease characterized by the gradual deterioration of cartilage, typically impacting individuals aged 50 and above, although it can also occur in younger people. The condition progresses slowly, with symptoms intensifying over time, leading to significant pain and discomfort. Early diagnosis and intervention can significantly alleviate pain and enhance the quality of life for patients. Recent advancements in medical imaging, particularly X-ray technology, have shown considerable promise in supporting the diagnosis of knee osteoarthritis. This paper introduces an automated deep learning- based ordinal classification system designed for the detection and classification of knee osteoarthritis. The proposed system introduces an innovative approach utilizing deep learning for the automated detection and ordinal classification of knee osteoarthritis based on X-ray images. It aims to achieve high accuracy, offering critical insights to medical professionals for making informed decisions regarding patient care and treatment strategies. By leveraging the power of deep learning, this approach aims to achieve high accuracy in diagnosis, thereby providing critical insights for medical practitioners. These insights can inform better decision-making in patient care and treatment planning, ultimately leading to improved outcomes for those suffering from knee osteoarthritis.
Keywords: Deep learning, Osteoarthritis, classification, severity, radiographic changes, accuracy
[This article belongs to International Journal of Biomedical Innovations and Engineering(ijbie)]
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| Volume | 02 |
| Issue | 02 |
| Received | July 16, 2024 |
| Accepted | July 24, 2024 |
| Published | July 25, 2024 |
