Prediction of severity of Knee Osteoarthritis on X-ray images using deep learning

The most prevalent kind of arthritis is osteoarthritis (OA). Radiologists use to employ the Kellgren-Lawrence (KL) grading system to identify the aggressiveness of OA based on the information shown on the pair of knee joints. Computer-assisted strategies have recently been proposed to enhance the accuracy of OA diagnosis. Previous semiautomatic segmentation approaches, on the other hand, required human interaction, limiting their use on huge datasets. Furthermore, CNN is used to quantify OA rigorousness to investigate the relationships among distinct local regions. SSD reduces human interaction and provides a back-to-back approach to computerized osteoarthritis detection by incorporating the object detection model. The rating is based on X-ray scans from the Osteoarthritis Initiative (OAI) dataset. At the cost of training on a huge dataset with over 8260 knee joint samples, our method accurately segments 96.37% of data.