Automated Knee Osteoarthritis Classification from X-ray Images Using the VGG-16 Model

Knee arthritis is the most frequent ailment among the senior population. This illness affects a large number of people worldwide. The biggest issue is with the joints. The higher joints are the femur, the lower joints are the tibia, and the patella is the kneecap. There is cartilage loss, which causes a difficulty with mobility. To diagnose this condition manually, knee scans are analysed and divided into five groups using the Kellgren-Lawrence (KL) approach. This process requires extensive healthcare expertise, significant experience, and considerable time, yet it remains susceptible to errors. Consequently, the era of artificial intelligence has arrived. AI is making a profound impact on the healthcare sector.This work classified the KOA using the publicly accessible OAI (Osteoarthritis Initiative) dataset. This work primarily uses deep learning, a specialisation of AI, for the categorisation and severity detection of the Knee Osteoarthritis. This study primarily uses the VGG-16 DNN model for binary classification as well as multiclassification. Using this model results in optimised efficiency and higher accuracy than previous models. In the future, we will work with genuine data collected from numerous hospitals.