ABSTRACT:

Osteoarthritis (OA) is a degenerative joint disease that affects millions worldwide, leading to pain and reduced mobility. Early detection is crucial for timely intervention and improved patient outcomes. This project focuses on the automatic detection of osteoarthritis using deep learning-based image classification. Three pre-trained convolutional neural network (CNN) models—VGG19, RESNET50, and MobileNetV2—are employed for feature extraction and classification of medical images. The models are fine-tuned and evaluated on a dataset of knee X-ray images to determine their effectiveness in diagnosing OA. Performance metrics such as accuracy, precision, recall, and F1-score are used to compare the models. The results of this study can contribute to the development of AI-assisted diagnostic tools for osteoarthritis detection.

INTRODUCTION:

Osteoarthritis (OA) is a chronic, degenerative joint disorder that affects millions of people worldwide, leading to pain, stiffness, and reduced mobility. It primarily targets the articular cartilage, which serves as a protective cushion between bones, allowing smooth joint movement. Over time, as the cartilage deteriorates, the bones begin to rub against each other, causing inflammation, pain, and joint deformities. The knee joint is particularly susceptible to OA due to its weight-bearing nature and continuous mechanical stress, making knee osteoarthritis one of the most common forms of arthritis-related joint degeneration.

Causes and Risk Factors:

Knee osteoarthritis can develop due to various factors, including aging, obesity, genetic predisposition, previous joint injuries, and mechanical stress on the knee. While aging is a significant risk factor, younger individuals, particularly athletes or those with physically demanding occupations, may also develop OA due to repetitive stress or trauma to the knee joint. Other contributing factors include metabolic disorders and inflammatory conditions such as rheumatoid arthritis, which can exacerbate cartilage degradation.

Clinical Manifestations and Diagnosis:

The symptoms of knee OA vary depending on the severity of the disease but commonly include joint pain, stiffness (especially in the morning or after inactivity), swelling, reduced range of motion, and a grinding or clicking sensation within the joint. As the disease progresses, mobility declines, significantly impacting the patient's quality of life. The standard method for diagnosing OA involves clinical evaluation, medical history, and radiographic imaging, where X-rays are used to assess joint space narrowing, osteophyte formation, and bone sclerosis.

Kellgren-Lawrence (KL) Grading System and Dataset Overview
The severity of knee osteoarthritis is classified using the Kellgren-Lawrence (KL) grading system, which evaluates radiographic features to assign a grade from 0 to 4:
• Grade 0 (Healthy): No signs of osteoarthritis, normal knee joint.
• Grade 1 (Doubtful): Possible osteophytic lipping with minimal joint space narrowing.
• Grade 2 (Minimal): Presence of definite osteophytes with possible narrowing of the joint space.
• Grade 3 (Moderate): Multiple osteophytes, clear joint space narrowing, and mild sclerosis.
• Grade 4 (Severe): Large osteophytes, significant joint space narrowing, severe sclerosis, and joint deformity.

The dataset used in this study consists of knee X-ray images labeled according to the KL grading system, providing a comprehensive foundation for deep learning-based osteoarthritis detection. This dataset is crucial for developing artificial intelligence models capable of accurately detecting and classifying OA severity, aiding in early diagnosis and intervention.
Importance of Early Detection and AI Integration

Early detection of osteoarthritis is essential to delay disease progression and improve patient outcomes. Traditional diagnostic methods rely on expert radiologists, but with advancements in deep learning and medical imaging, AI-driven approaches have shown great potential in automating OA detection. By leveraging pre-trained convolutional neural networks (CNNs) such as VGG19, RESNET50, and MobileNetV2, this study aims to develop an efficient AI-based system for classifying knee osteoarthritis severity. These models can extract intricate features from X-ray images, enabling precise classification and assisting healthcare professionals in decision-making.
This integration of AI in medical diagnosis can lead to faster, more reliable, and cost-effective OA detection, reducing the burden on radiologists and improving patient care. With further research and optimization, AI-assisted osteoarthritis detection can be a game-changer in orthopedic healthcare, paving the way for more accurate, accessible, and early disease diagnosis.