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Open Access
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Suman Rani, Minakshi Memoria
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- Associate Professor, Research Scholar Mechanical Engineering, Uttaranchal University, Mechanical Engineering, Uttaranchal University Uttarakhand, Uttarakhand India
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Abstract
nOsteoarthritis (OA) is the most common kind of arthritis. By analysing data from both sides of the knee joints, radiologists use the Kellgren–Lawrence (KL) grading system to determine the severity of osteoarthritis (OA). The need for knee arthroplasties has increased as a result of this. Recently, there have been proposals for computer-assisted techniques to improve the precision of OA diagnosis. Choosing between conservative and surgical treatment options for knee osteoarthritis (OA) requires an accurate diagnosis and classification of the condition. Consequently, standard weight- bearing knee X-rays are heavily relied upon by many orthopaedic surgeons. Enhancing these tests’ consistency and repeatability can have major benefits. Recent developments in artificial intelligence (AI) have shown promise, especially in the area of deep learning. Earlier, the diagnosis of knee osteoarthritis was done using deep learning and DNN. But in the future, in the diagnosis of knees, it will use computer vision techniques. The purpose of this research is to evaluate how well artificial intelligence (AI) can classify the degree of osteoarthritis M(OA) in the knee by taking into account entire image sets and taking common visual anomalies like casts, implants, and non-degenerative diseases into account. In this study, we present an architectural model—based on the ViT transformer—for categorising osteoarthritis in the knee. We will use ViT to classify knee osteoarthritis in the future and gather information from multiple sources. In this study discuss the how ViT used in knee osteoarthritis diagnosis
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Keywords: Knee Osteoarthritis classification, Deep learning, KL Grade, AI, Image Processing, Vision Transformer
n[if 424 equals=”Regular Issue”][This article belongs to Journal of Mechatronics and Automation(joma)]
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Volume | 11 | |
[if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | 01 | |
Received | April 9, 2024 | |
Accepted | April 22, 2024 | |
Published | May 15, 2024 |
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