Alshamrani K, Alshamrani HA. Lossless compression-based detection of osteoporosis using bone X-ray imaging.
JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2024;
32:475-491. [PMID:
38393881 DOI:
10.3233/xst-230238]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/25/2024]
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.
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