Knee osteoarthritis grading using deep learning classifier

Ng, Jevyline (2024) Knee osteoarthritis grading using deep learning classifier. Final Year Project, UTAR.

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Abstract

This project is a Deep Learning Model research project for academic purpose. Knee osteoarthritis (OA) is a prevalent degenerative joint disorder affecting millions worldwide, leading to pain, impaired mobility, and diminished quality of life. Accurate grading of OA severity is crucial for effective clinical management, yet it remains a challenging task prone to subjectivity and inter-observer variability. In this study, we propose a novel approach utilizing deep learning classifiers to automate the grading process of knee OA based on the Kellgren Lawrence grading system. Through the development and evaluation of multiple deep learning models, we aim to provide a robust and reliable tool for clinicians to objectively assess OA severity from X-ray images. Our methodology involves preprocessing of X-ray images, followed by feature extraction and classification using convolutional neural networks (CNNs). The performance of each model is assessed through rigorous validation on a diverse dataset of knee X-ray images annotated with ground truth Kellgren Lawrence grades.

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