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Research Article: Detection of Carious Lesions and Restorations Using Particle Swarm Optimization Algorithm

机译:研究文章:使用粒子群优化算法检测龋病病变和修复

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Background/Purpose. In terms of the detection of tooth diagnosis, no intelligent detection has been done up till now. Dentists just look at images and then they can detect the diagnosis position in tooth based on their experiences. Using new technologies, scientists will implement detection and repair of tooth diagnosis intelligently. In this paper, we have introduced one intelligent method for detection using particle swarm optimization (PSO) and our mathematical formulation. This method was applied to 2D special images. Using developing of our method, we can detect tooth diagnosis for all of 2D and 3D images. Materials and Methods. In recent years, it is possible to implement intelligent processing of images by high efficiency optimization algorithms in many applications especially for detection of dental caries and restoration without human intervention. In the present work, we explain PSO algorithm with our detection formula for detection of dental caries and restoration. Also image processing helped us to implement our method. And to do so, pictures taken by digital radiography systems of tooth are used. Results and Conclusion. We implement some mathematics formula for fitness of PSO. Our results show that this method can detect dental caries and restoration in digital radiography pictures with the good convergence. In fact, the error rate of this method was 8%, so that it can be implemented for detection of dental caries and restoration. Using some parameters, it is possible that the error rate can be even reduced below 0.5%.
机译:背景/目的。就牙齿诊断的检测而言,直到现在没有智能检测。牙医只看图像,然后他们可以根据他们的经验检测牙齿的诊断位置。使用新技术,科学家们将智能地实施牙齿诊断的检测和修复。在本文中,我们已经推出了一种使用粒子群优化(PSO)和数学制定的一种智能方法。该方法应用于2D特殊图像。使用我们的方法的开发,我们可以检测所有2D和3D图像的牙齿诊断。材料和方法。近年来,可以在许多应用中通过高效优化算法实现图像的智能处理,特别是用于检测龋齿和恢复而没有人为干预。在本作工作中,我们用我们的检测公式解释PSO算法,以检测龋齿和恢复。图像处理也有助于我们实现我们的方法。为此,使用牙齿的数字射线照相系统拍摄的照片。结果与结论。我们为PSO的健身实施了一些数学公式。我们的研究结果表明,该方法可以检测数字射线照相图片中的龋齿和恢复,良好的收敛性。实际上,这种方法的错误率为8%,因此可以实现它以检测龋齿和恢复。使用一些参数,可能甚至可以降低0.5%的错误率。

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