Deep Neural Models for Early Diagnosis of Knee Osteoarthritis and Severity Grade Prediction

Abstract

Osteoarthritis (OA) is a type of arthritis that results in malfunction and eventual loss of the cartilage of joints. It occurs when the cartilage that cushions the ends of the bones wear out. OA is the most common joint disease which frequently occurs after the age of 45 in case of males and 55 in the case of females. Manual detection of OA is a tedious and labour-intensive task and is performed by trained specialists. We propose a fully automated computer-aided diagnosis system to detect and grade osteoarthritis severity as per the Kellgren-Lawrence (KL) classification. In this paper, we experiment with various approaches for automated OA detection from X-ray images. Image-level information such as content descriptors and image transforms are identified and assigned weights using Fisher scores. KL-grade is then projected using weighted nearest neighbours, and different stages of OA severity are classified. Pre-processing, segmentation, and classification of the X-ray images are achieved using data augmentation, deep neural network, and residual neural networks. We present experimental results and discussion with respect to the best-performing models in our experiments. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.