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Issue title: Special Section: Medical Applications of X-ray Imaging Techniques
Article type: Research Article
Authors: Alshamrani, Khalafa; b; * | Alshamrani, Hassan A.a
Affiliations: [a] Department of Radiological Sciences, College of Applied Medical Sciences, Najran University, Najran, Saudi Arabia | [b] Department of Oncology and Metabolism, School of Medicine and Population Health, University of Sheffield, Sheffield, United Kingdom
Correspondence: [*] Corresponding authors: Khalaf Alshamrani, Department of Radiological Sciences, College of Applied Medical Sciences, Najran University, Najran, Saudi Arabia. E-mail: [email protected].
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.
Keywords: Lossless compression, classification, Bone Xray, ROI, patch size, osteoporosis
DOI: 10.3233/XST-230238
Journal: Journal of X-Ray Science and Technology, vol. 32, no. 2, pp. 475-491, 2024
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