Knee Osteoarthritis Detection and Severity Prediction Using Convolutional Neural Network

Abstract

Abstract: The worst kind of arthritis is knee osteoarthritis (KOA). If left untreated, it can require a knee substitute. Right away KOA diagnosis is therefore essential for the best possible care. The manual detection of KOA is a laborious and susceptible to mistakes process. Computational methods are required for timely and accurate detection. The failure of the connective tissue in the knee joint, which results in bone fragments rubbing against other bones, leads to osteoarthritis of the knee. The wear and tear hurt, stiffens, and inflames the knees. Even though osteoarthritis of the knee cannot be cured, there are several treatments that can help to reduce symptoms and decrease the condition's development. A radiologist grades the anomalies on knee X-ray pictures according to their severity using Kellgren-Lawrence's five-point ordinal scale (0–4). The datasets must be trained first using the CNN approach, which is used in this study. Various convolutional layers emerge on the CNN algorithm during training, and the precision increases with each layer. Once uploaded, the X-ray image is shrunk, its color is turned to grey, and several Convolutional layers are applied to it with the help of the CNN algorithm.