Authors : Ashadu Jaman Shawon; Ibrahim Ibne Mostafa Gazi; Humaira Rashid Hiya; Ajoy Roy

Volume/Issue : Volume 9 - 2024, Issue 4 - April

Google Scholar : https://tinyurl.com/3jtvpwjx

Scribd : https://tinyurl.com/d2auc36u

DOI : https://doi.org/10.38124/ijisrt/IJISRT24APR2565

Abstract : Low bone mass and structural degradation are the hallmarks of osteoporosis, a disorder that increases the risk of fractures, especially in the elderly. For prompt intervention and fracture prevention, early identification is essential. However, osteoporosis is frequently not detected until advanced stages by existing diagnostic techniques. In order to overcome this difficulty, scientists suggest using machine learning to automatically identify osteoporosis early in X-ray pictures. Utilizing two cutting- edge convolutional neural network architectures, ResNet50 and VGG16, their system was pretrained on extensive datasets and refined on a carefully selected dataset of X-ray pictures. When identifying images as suggestive of osteoporosis or normal bone density, the ResNet50 model showed an accuracy of 98%, whereas the VGG16 model achieved 78% accuracy. By combining these models and using sophisticated image segmentation methods, the system detects early osteoporosis indications with an overall accuracy of 96%. This automated method has the potential to decrease the incidence of fractures linked to osteoporosis, enable early treatment initiation, and increase the rate of early diagnosis.

Keywords : Osteoporosis, Machine learning, prediction, ResNet50, VGG16.

References :

1. Yang, Y., Zhang, Y., & Zhang, Z. (2019). Osteoporosis Detection from X-ray Images Using Convolutional Neural Networks. IEEE Access, 7, 118927-118934. DOI: 10.1109/ACCESS.2019.2932429
2. Lee, S., Park, J. H., & Kim, J. (2020). Osteoporosis Detection from Hand Radiographs Using Deep Learning Techniques. IEEE Transactions on Medical Imaging, 39(5), 1663-1671. DOI: 10.1109/TMI.2019.2957311
3. Raj, A., & Jayasree, T. (2021). Detection of Osteoporosis in X-ray Images using Deep Learning. International Journal of Engineering Research & Technology, 10(5), 637-641. DOI: 10.18178/ijert.10.5.637-641
4. Karthik, K., Rajasekaran, M. P., & Mohanapriya, K. (2020). Early Detection of Osteoporosis from Bone X-ray Images using Deep Learning Techniques. International Journal of Computer Applications, 173(3), 1-5. DOI: 10.5120/ijca2020919194
5. Patel, R., & Patel, V. (2019). Detection of Osteoporosis using Deep Learning Techniques on X-ray Images. International Journal of Advanced Research in Computer Science, 10(4), 176-180. DOI: 10.26483/ijarcs.v10i4.6639
6. Kaggle. (n.d.). Kaggle datasets. Retrieved from https://www.kaggle.com/datasets
7. Shawon, A. J., Tabassum, A., & Mahmud, R. . (2024). Emotion Detection Using Machine Learning: An Analytical Review. Malaysian Journal of Science and Advanced Technology, 4(1), 32–43. https://doi.org/10.56532/mjsat.v4i1.195
8. Smith, A. B., & Jones, C. D. (2020). Deep learning for medical image analysis: A review. Journal of Medical Imaging, 7(1), 011001.
9. Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., & Summers, R. M. (2019). ChestX-ray8: Hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2097-2106).
10. Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
11. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).