Deep learning based knee osteoarthritis detection and classification

P. M. K. Prasad; Karri Chiranjeevi

Author(s)

P. M. K. Prasad Department of Electronics and Communication Engineering, GVP College of Engineering for Women,Visakhapatnam, Andhra Pradesh,India

Karri Chiranjeevi Department of Electronics and Communication Engineering, AU College of Engineering, Andhra University,Visakhapatnam, Andhra Pradesh,India

Abstract

Knee Osteoarthritis (KOA) is a prevalent degenerative joint disease that effects millions of people worldwide, causing pain and disability. This condition occurs when the cartilage in the knee joints wears away overtime, leading to bone-on-bone contact, which can result in pain, stiffness, swelling, and decreased range of motion. The diagnosis of OA is primarily carried out by evaluating symptoms and comparing plain radiographs, which can be subjective. However, deep learning techniques, such as Convolution Neural Networks (CNNs), have emerged as a promising solution to medical problems in recent years. Therefore, the goal of this study is to develop and implement a classification system that can aid doctors in reducing their workload and assist radiologists in assessing the severity of the pain accurately. Furthermore, this will enable them to make the best diagnosis and recommend the most appropriate treatment. In this paper, Deep Neural Networks (DNN), especially Convolutional Neural Networks (CNN) with the Transfer Learning approach, are used. Based on the X-ray images, the grading system is used to assess the severity of OA in the knee. The performance of the method is evaluated with the help of the Knee Osteoarthritis Dataset. This dataset provides a comprehensive collection of X-ray images depicting knees at various stages of osteoarthritis progression.

Publication Details

Page(s) 394-400

DOI DOI not available

Published Journal: ARPN Journal of Engineering and Applied Sciences, Volume: 20, Issue: 7, Year: 2025

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Keywords

deep learning, Convolutional neural networks, efficient net

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