Feature-Driven Deep Learning for Osteoporosis Detection: Leveraging Explainable AI
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2026-01-01
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16879724
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16879732
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2-s2.0-105045128122
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Applied Computational Intelligence and Soft Computing
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2026
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1
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SCOPUS
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Applied Computational Intelligence and Soft Computing Vol.2026 No.1 (2026)
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Hosain A.K.M.S., Zereen A.N. Feature-Driven Deep Learning for Osteoporosis Detection: Leveraging Explainable AI. Applied Computational Intelligence and Soft Computing Vol.2026 No.1 (2026). doi:10.1155/acis/8214937 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/118156
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Feature-Driven Deep Learning for Osteoporosis Detection: Leveraging Explainable AI
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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, a deep learning framework utilizing convolutional neural networks (CNNs) to detect osteoporosis from knee X-ray images is presented. Two models: MobileNet V3 and EfficientNet B0 are employed and fine-tuned on a dataset of 774 knee X-ray images. Model performance is further enhanced through the curation of a proposed dataset, wherein critical features are emphasized utilizing Explainable AI (XAI) frameworks (LIME and Grad-CAM). The results show that while both models achieved an accuracy of 0.77 on the original dataset, EfficientNet B0 consistently outperformed MobileNet V3 on the proposed 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.