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Tooth Anatomy01:21

Tooth Anatomy

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The human tooth enables us to eat a variety of foods, speak clearly, and even aid in shaping our faces. Teeth are composed of various elements that work together. Here's a detailed look at the anatomy of a human tooth.
The Crown, Neck, and Root
The visible part of the tooth is referred to as the crown. It's covered by enamel, the hardest substance in the human body. The crown is uniquely shaped for each type of tooth, allowing for different functions such as cutting, tearing, or...
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Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures
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针对ACO优化的MobileNetV2-ShuffleNet混合模型用于自动化牙虫分类.

Kotturu Kaveri1, Venkata Ratna Prabha K1, G Pradeep Reddy2

  • 1Department of Electronics and Communication Engineering, Siddhartha Academy of Higher Education, Deemed to be University, Kanuru, Vijayawada, Andhra Pradesh, 520007, India.

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概括

这项研究引入了一种自动化方法,用于使用全景X射线对牙虫进行分类. 这种新的方法提高了诊断的准确性,改善了口腔感染的早期检测.

关键词:
这就是ACO ACO ACO.牙腐烂是一种牙腐烂.移动网络V2 移动网络V2全景X射线图片 全景X射线图片这就是ShuffleNet.索贝尔 - 费尔德曼边缘检测检测器

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科学领域:

  • 牙科 牙科是指牙科的专业.
  • 医疗成像医学成像
  • 人工智能的人工智能

背景情况:

  • 如果不及时诊断,牙感染会给健康带来重大风险.
  • 从X射线图像中诊断口腔感染是具有挑战性的,因为细微的解剖学差异和数据不平衡.
  • 准确及时诊断牙损伤对于有效治疗至关重要.

研究的目的:

  • 开发一种使用全景放射图像进行牙腐烂分类的强大和自动化的方法.
  • 为了应对阶级不平衡和牙X射线中的弱解剖差异的挑战.
  • 通过人工智能提高牙科诊断的准确性和可靠性.

主要方法:

  • 预处理技术包括数据平衡的聚类和 Sobel-Feldman 边缘检测用于功能增强.
  • 开发一种混合深度学习架构,将MobileNetV2和ShuffleNet.net结合起来.
  • 集成殖民地优化 (ACO) 算法用于参数调整和全球搜索优化.

主要成果:

  • 独立的MobileNetV2和ShuffleNet模型显示了有限的分类能力.
  • 混合架构显著提高了分类精度.
  • 增强ACO的混合方法实现了92.67%的高精度,优于单个网络的性能.

结论:

  • 拟议的ACO增强混合模型在牙损分类方面表现出卓越的性能.
  • 这种自动化方法为牙医提供了可靠的工具,提高了诊断效率.
  • 该方法有可能在自动牙科诊断系统中得到广泛应用.