Publication

Abstract :

Osteoarthritis (OA) is an existing degenerative joint disease with a potential societal impact, requiring accurate and early diagnosis for effective treatment. In this work, we have deployed deep learning techniques, specifically ResNet50 and VGG16, to predict osteoarthritis from medical imaging data. Remarkably, our models produced an impressive accuracies of 91.0% and 89.5% respectively, showing the efficacy of CNN in OA classification. To further better the interpretability in the predictions of our model,we used GradCam++ visualization, a technique that enabled us in generating heat maps that highlights the regions crucial for model decision-making. Optimizing GradCam++, we noticed an improvement in accuracy to 93.0%, reinforcing its value in refining model interpretability. The generated heat maps are used not only in understanding the model validation but also in providing clinicians with useful information regarding the areas of concern within the medical images. Our outcomes shows the ability of combining deep learning techniques with visualization tools like GradCam++ to enhance predictive accuracy and to offer trustable and interpretable results. The areas of concern in the heat maps throw light on the specific features influencing the model's decision. This combined method promises for advancing the field of medical image analysis, fostering more accurate and clinically applicable diagnostic tools for osteoarthritis and potentially other medical conditions.