eng Oriental Scientific Publishing Company Biomedical and Pharmacology Journal 0974-6242 2025-02-20 18 March Spl Edition 217 227 10.13005/bpj/3083 63968 article Suman Rani 1 Minakshi Memoria 3 Kapil Joshi 4 Anil Kumar Lamba 5 Department of Computer Science, Uttaranchal University, Dehradun Department of Information Technology, IIMT College of Engineering, Greater Noida Department of Computer Science, King Khalid University, Saudi Arabia Department of Computer Science, Uttaranchal University, Dehradun Department of Computer Science, MMEC, Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala 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. https://biomedpharmajournal.org/vol18marchspledition/automated-knee-osteoarthritis-classification-from-x-ray-images-using-the-vgg-16-model/ CNN Deep Neural Networks KOA VGG-16 X-Ray OAI