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Today, in dentistry, computer-based techniques, such as preoperative planning and planning, and implant and surgical evaluation, are increasingly being developed. In order to achieve and implement the mentioned processes, the automatic segmentation of teeth is one of the important and primary steps. Segmentation of teeth is also used in the field of identity recognition, planning for orthodontics, and facial cosmetic surgery. Also, the separation of dental structures is very important from the anatomical and pathological point of view, which can be achieved by using segmentation. The approach of the present study to segment teeth consists of two modules, the first module is designed to identify teeth and the second module is designed to number teeth. The Faster R-CNN network is used to design the tooth recognition module, and the tooth numbering module is based on the VGG-16 convolution architecture. In this article, a database containing panoramic dental images is used to train and test the grid, and the grid is used to predict the tooth number, according to the FDI two-digit numbering system. The results of the system implementation showed that the accuracy of the proposed model in the dental diagnosis stage is 89.8% and in the tooth numbering stage is 86.5%, which indicates an improvement in the performance of the proposed system compared to previous methods. Due to the proper functioning of the system, it can be used for automated dental systems. The results of this study can help dentists to obtain more and more accurate information from radiographic images. [ABSTRACT FROM AUTHOR]

In this paper, we report on the detection and measurement of refractive properties of healthy and cancerous human liver samples taken from real data using a prism pair technique that covers the near-visible and infrared spectral range. Then, according to real samples, an all-optical refractive index biosensor is designed and simulated. Newly used data on the wavelength-dependent refractive index of human liver tissues are presented and detected under different conditions. Remarkably, the real index contrast is mentioned as an index of the difference between normal liver tissue and liver metastases. According to real data from the human body, a special account can be made on the detection of the healthy and cancerous liver in this article. Numerous photon crystal-based biosensors are proposed for various diseases, but no serious research has been done on unhealthy livers, and this article tries to cover this type of disease as well. The obtained sensitivity values are reported as 1000 nm/RIU for the given bio samples. [ABSTRACT FROM AUTHOR]