当患者的医疗情况不全面时,确定用于安装牙种植体的正确附件是影响假牙的可持续性和可靠性的重要因素。牙医需要从X射线图像中识别植入物制造商,以确定进一步的治疗程序。在排队等待治疗的患者的缩放量下,确定制造商是一项高压任务。为了减轻医生的负担,基于新提出的具有按需客户端-服务器结构的更薄VGG模型,建立了牙种植体识别系统。我们提出了一种更薄的VGG16版本,称为TVGG,通过减少密集层中的神经元数量来提高系统的性能,并从牙科射线照相图像中有限的纹理和图案中获得优势。将所提出的系统的结果与原始预训练的VGG16进行比较,以验证所提出的系统的可用性。
Identifying the right accessories for installing the dental implant is a vital element that impacts the sustainability and the reliability of the dental prosthesis when the medical case of a patient is not comprehensive. Dentists need to identify the implant manufacturer from the x-ray image to determine further treatment procedures. Identifying the manufacturer is a high-pressure task under the scaling volume of patients pending in the queue for treatment. To reduce the burden on the doctors, a dental implant identification system is built based on a new proposed thinner VGG model with an on-demand client-server structure. We propose a thinner version of VGG16 called TVGG by reducing the number of neurons in the dense layers to improve the system\'s performance and gain advantages from the limited texture and patterns in the dental radiography images. The outcome of the proposed system is compared with the original pre-trained VGG16 to verify the usability of the proposed system.