Deep Learning Based Severity Grading of Knee Osteoarthritis Based on Radiological Images

Abstract

Abstract: Knee OA is a prevalent degenerative joint condition affecting a significant portion of the global population, and timely identification and accurate grading are crucial for optimal patient care and treatment planning. This system aimed at developing an advanced clinical decision support system for the early detection and precise grading of knee osteoarthritis (OA) using cutting-edge deep learning techniques. Traditionally, the diagnosis of knee osteoarthritis has relied on manual assessments of radiological images, which can be resource-intensive and subject to variations among evaluators. Deep learning, a subset of artificial intelligence, offers a promising solution by automating this process with exceptional precision. The core objectives of this project include the development of a CNN model based on the EfficientNet-B5 architecture, designed to autonomously analyze radiological images of knee joints. Additionally, a user-friendly web-based interface is being constructed to facilitate image uploads and present severity grades. The project involves rigorous training and fine-tuning of the EfficientNet-B5 using a dataset of knee OA images to achieve a high degree of accuracy in severity grading. The system will ultimately be deployed on a scalable and secure cloud platform for practical utility.