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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. [ABSTRACT FROM AUTHOR]

The article "Diagnosing osteoporosis using deep neural network-assisted optical image processing method" published in Optical & Quantum Electronics has been retracted by the Publisher due to concerns about compromised peer-review process, inappropriate references, and being out of scope for the journal. The corresponding author, Mohammad Khursheed Alam, disagrees with the retraction. The Publisher, in consultation with the Editors-in-Chief, no longer has confidence in the results and conclusions of the article. [Extracted from the article]