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Lossless compression-based detection of osteoporosis using bone X-ray imaging.
Lossless compression-based detection of osteoporosis using bone X-ray imaging.
- Source :
-
Journal of X-Ray Science & Technology . 2024, Vol. 32 Issue 2, p475-491. 17p. - Publication Year :
- 2024
-
Abstract
- BACKGROUND: Digital X-ray imaging is essential for diagnosing osteoporosis, but distinguishing affected patients from healthy individuals using these images remains challenging. OBJECTIVE: This study introduces a novel method using deep learning to improve osteoporosis diagnosis from bone X-ray images. METHODS: A dataset of bone X-ray images was analyzed using a newly proposed procedure. This procedure involves segregating the images into regions of interest (ROI) and non-ROI, thereby reducing data redundancy. The images were then processed to enhance both spatial and statistical features. For classification, a Support Vector Machine (SVM) classifier was employed to distinguish between osteoporotic and non-osteoporotic cases. RESULTS: The proposed method demonstrated a promising Area under the Curve (AUC) of 90.8% in diagnosing osteoporosis, benchmarking favorably against existing techniques. This signifies a high level of accuracy in distinguishing osteoporosis patients from healthy controls. CONCLUSIONS: The proposed method effectively distinguishes between osteoporotic and non-osteoporotic cases using bone X-ray images. By enhancing image features and employing SVM classification, the technique offers a promising tool for efficient and accurate osteoporosis diagnosis. [ABSTRACT FROM AUTHOR]
- Subjects :
- *X-ray imaging
*X-rays
*DEEP learning
*OSTEOPOROSIS
*SUPPORT vector machines
Subjects
Details
- Language :
- English
- ISSN :
- 08953996
- Volume :
- 32
- Issue :
- 2
- Database :
- Academic Search Index
- Journal :
- Journal of X-Ray Science & Technology
- Publication Type :
- Academic Journal
- Accession number :
- 176365879
- Full Text :
- https://doi.org/10.3233/XST-230238