Abstract: Knee osteoarthritis (OA) is one of the leading causes of disability, affecting millions of people and limiting their ability to carry out daily activities. Diagnosis is usually performed by analyzing X-ray or MRI scans, but the manual process depends heavily on expert interpretation, which can vary between clinicians and take significant time. To overcome these challenges, this project introduces an automated system that applies deep learning methods, specifically Convolutional Neural Networks (CNNs) enhanced with AlexNet, to identify and grade knee OA. The system is designed to process medical images through steps of preprocessing, augmentation, and feature extraction before classification. It further predicts OA severity using the Kellgren–Lawrence (KL) grading scale. Performance is assessed with metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. By aiming for an accuracy of at least 90%, this model seeks to provide a reliable decision-support tool that reduces subjectivity, speeds up diagnosis, and supports early treatment planning in clinical practice.

Keywords: Knee Osteoarthritis, Convolutional Neural Network, AlexNet, Deep Learning, Medical Imaging, Feature Extraction, Kellgren–Lawrence Scale, Automated Diagnosis, Transfer Learning, Clinical Support System.

Downloads: | DOI: 10.17148/IARJSET.2025.12836