Knee Osteoarthritis Detection Using Deep Learning

Abstract

In the area of human health, middle-aged and older people who have osteoarthritis in their knees as a result of poor eating habits, inactivity, lack of physical sports, etc., Once knee osteoarthritis is identified, the best treatment options include physiotherapy, exer cises, and lifestyle changes rather than pharmaceutical medicines. Early identification is the most effective strategy to limit the progression of osteoarthritis in the knee. At the moment, medical experts use X-ray imaging to anticipate osteoarthritis in the knee. However, because radiologists often lack experience, the guide X-ray approach might also result in incorrect interpretation. As scientific study on machine learning and deep learning advances, osteoarthritis from X-ray pictures may be effectively predicted. Still, the majority of these approaches aim for increased prediction accuracy in order to identify osteoarthritis early on. The machine learning algorithms that are currently being proposed have a 94% prediction accuracy in identifying osteoarthritis. Additionally, recommend pre-trained models for knee osteoarthritis (OA) images from the Osteoarthritis Initiative (OAI) dataset called ResNet101 based on bypass connections. The ResNet101 bypass link is utilized to overcome the vanishing gradient problems.