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Diagnosing osteoporosis using deep neural networkassisted optical image processing method
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DOI：10.1007/s11082-023-06031-w.png)
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Osteoporosis is a disease in which bone mass and structural strength decrease, leading to increased fragility and susceptibility to fractures in the face, neck, spine, wrist, etc. It's a disease that doesn't display any symptoms until a break occurs; in other words, it's a silent illness. Sometime it also utilised frequently in dentistry application such that to detect osteoporosis during the operation or implanting on maxilla. This body of work shows that, despite major breakthroughs in medicine, there remains a unique role for the development of novel tools for the diagnosis of osteoporosis. In this work, we present an osteoporosis detection system developed using image processing and support vector machine (SVM) techniques. Compared to prior studies in this area, the data collected by the technology established in this study-which includes 50 sample photographs of the tibia-is of high quality. The proposed method achieves an impressively high level of accuracy (83.6% with 50 samples) because to the inclusion of the histogram feature and the use of tissue properties during feature extraction.
Keyword:
Osteoporosis
Neural networks
Support vector machines SVM
Image processing
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