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: Kaalshamrani@nu.edu.sa.
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
