Knee Osteoarthritis Detection Using X-Rays and DNN

Knee osteoarthritis (OA) is a prevalent degenerative joint disease that affects millions worldwide, leading to pain, disability, and reduced quality of life. Early detection and intervention are crucial for effective management and prevention of disease progression. In this paper, we propose a novel approach for knee OA detection using X-ray images and deep neural networks (DNNs). Specifically, we investigate the performance of two popular DNN architectures: EfficientNet-B5 and DenseNet121. We begin by pre-processing a dataset of knee X-ray images to extract relevant features and mitigate noise. Subsequently, we fine-tune the pre-trained EfficientNet-B5 and DenseNet121 models using transfer learning techniques to adapt them to the task of knee OA detection. We then evaluate and compare the performance of these models in terms of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC). Our experimental results demonstrate the efficacy of both EfficientNet-B5 and DenseNet121 architectures in knee OA detection, outperforming traditional machine learning approaches. However, we observe variations in their performance metrics, indicating nuanced differences in their ability to capture and discriminate pathological features in knee X-ray images. Furthermore, we conduct an in-depth analysis to understand the factors contributing to the performance disparities between the two models. Overall, our study contributes to the growing body of research aimed at leveraging DNNs for medical image analysis tasks, particularly in the domain of musculoskeletal disorders. The insights gained from this research can potentially aid clinicians in early diagnosis and personalized treatment planning for knee OA patients, ultimately improving patient outcomes and healthcare efficiency.