Deep Learning for early Identification and Prediction of Knee Osteoarthritis

Abstract

 Evaluation of persons with knee osteoarthritis (OA) whose structural progression is anticipated is essential to the development of medications that treat diseases in different ways. Deep learning approaches have rapidly gained popularity as the preferred tool for analyzing medical image segmentation. This study explores many contributions in the field of Deep Learning in medicine, covers the most common difficulties in recent years, and also analyzes the principles of Deep Learning ideas relevant to medical image categorization. We have used Deep Learning to make predictions from X-ray images. Deep Learning research can be applied to image classification, object identification, segmentation, registration, and other applications. Medical image analysis has a number of problems, such as poor picture enhancement and low image analysis dependability, that deep learning has been developed to define and solve. We propose research to address these current problems and advance the development of medical image segmentation. This feasibility research shows the interest in deep learning for osteoarthritis and could help even experienced radiologists with the difficult challenge of determining people who are at risk for illness development.