Feature-driven deep learning for Osteoporosis detection: leveraging explainable AI

Abstract

Osteoporosis, a widespread bone disorder affecting over 200 million people globally, is associated with significant morbidity, particularly due to increased fracture risk. While traditionally considered a condition afflicting the elderly, younger individuals are also susceptible. In this study, we present a deep learning framework utilizing Convolutional Neural Networks (CNNs) to detect osteoporosis from knee X-ray images. Two models, MobileNet V3 and EfficientNet B0, were employed and fine-tuned on a dataset of 774 knee X-ray images. We further improved model performance by curating a new dataset, emphasizing critical features using explainable AI (XAI). Our results show that while both models achieved an accuracy of 0.77 on the original dataset, EfficientNet B0 consistently outperformed MobileNet V3 on the new dataset with an accuracy of 0.9189 and an F1 score of 0.9315, compared to MobileNet V3’s accuracy of 0.8243 and F1 score of 0.8169. These findings demonstrate the effectiveness of CNNs, particularly EfficientNet B0, in accurately diagnosing osteoporosis from medical images, and underscore the importance of both model selection and feature-focused data preprocessing in improving diagnostic performance.