Lossless compression-based detection of osteoporosis using bone X-ray imaging.

Autor: Alshamrani K; Department of Radiological Sciences, College of Applied Medical Sciences, Najran University, Najran, Saudi Arabia.; Department of Oncology and Metabolism, School of Medicine and Population Health, University of Sheffield, Sheffield, United Kingdom., Alshamrani HA; Department of Radiological Sciences, College of Applied Medical Sciences, Najran University, Najran, Saudi Arabia.
Jazyk: angličtina
Zdroj: Journal of X-ray science and technology [J Xray Sci Technol] 2024; Vol. 32 (2), pp. 475-491.
DOI: 10.3233/XST-230238
Abstrakt: 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.
Databáze: MEDLINE
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