Paper Title
Severity of Osteoarthritis in the Knee Using a Multi-Scale Deep Convolutional Neural Network and Improved X-Ray Images
Abstract
Osteoarthritis (OA) is an extensive degenerative joint illness characterized by changes in bone structure and
cartilage degradation. Osteoarthritis (OA) is among the most common forms of arthritis, affecting millions of lives
worldwide. The proposed method is a deep learning-based framework that automatically assesses the severity of knee OA
through the use of Kellgren and Lawrence grade (KL grade) classification using knee X-rays. We use CNN models to
predict severity. The scarcity of datasets causes delays, in detecting osteoarthritis in its early stages. To address this
limitation, we aim to increase the dataset and enhance the X-ray images to detect the severity as early as possible. For
successful outcomes, the suggested approach takes into account several notable factors, such as jointspace narrowing,
osteophyte production, and bone deformation over time. This shows promise in improving diagnostic precision, enabling
early treatments, and offering customized therapies for OA victims.
Keywords - Knee Osteoarthritis, X-Ray, Deep Learning, KL-Grade