A Deep Learning Approaches for Knee Osteoarthritis Detection: Analysis of CNN Optimization and Fine-Tuning

An accurate and timely diagnosis of knee osteoarthritis, a prevalent condition that leads to joint pain and disability, is essential for effective treatment and management. Deep learning models have become power tools for medical image analysis in recent years, particularly in diagnosing conditions like knee osteoarthritis through X-ray imaging. However, their effectiveness is often constrained by the need for large and accurately annotated datasets. To overcome this limitation, fine-tuning pre-trained models, such as VGG16, alongside models and employing techniques like transfer learning and edge detection to enhance image features offer promising solutions. This study performs a comprehensive comparative analysis of these strategies, assessing their impact on improving the accuracy and reliability of deep learning models in diagnosing knee osteoarthritis. By exploring the synergy between these various methods, the research aims to identify the most effective combinations for developing more robust diagnostic tools in medical imaging. Notably, the fine-tuned CNN model achieved a remarkable accuracy of 96%, underscoring its potential in advancing knee osteoarthritis detection.