Automated Deep Learning Based Knee Osteoarthritis Joint Extraction and Classification

Knee osteoarthritis (KOA) is a widespread global condition, impacting over 300 million individuals as per the World Health Organization (WHO). Particularly prevalent among older adults, knee OA is a prominent cause of disability. Its occurrence increases with age, especially after 50, and is more frequent in women, particularly post-menopause. Several studies have been carried out so far for automated grading and classification of knee osteoarthritis (KOA), but none of them built strong foundations enough to make this system automated. This study focuses on machine-controlled knee joint extraction and grading classification with improved accuracy and performance. We used the osteoarthritis initiative (OAI) dataset of X-ray images for our study. Initially, a single-stage detector is used for joint extraction of the knee area as the X-ray images contain entire knees with both joints. Enhanced osteoarthritis feature extraction (OAFE) and osteoarthritis dimensionality reduction (OADR) blocks are used for grading classification. We have significantly improved state-of-the-art results. We have acquired joint extraction with a mean average precision (map) of 95.3% and grading classification accuracy of 78.93%. Furthermore, the performance due to the dimensionality reduction block has improved by a huge factor.