Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Dynamics of 4D supply chain system with fractal fractional derivatives insight of stability analysis and ANN prediction.

Scientific reports·2025
Same author

A comprehensive study of the novel 4D hyperchaotic system with self-exited multistability and application in the voice encryption.

Scientific reports·2024
Same author

Predicting individuals' preventive practices against Radon indoor exposure in Saudi Arabia: a cross sectional study.

Journal of radiological protection : official journal of the Society for Radiological Protection·2024
Same author

Comparative analysis and FPGA realization of different control synchronization approaches for chaos-based secured communication systems.

PloS one·2024
Same author

Caffeine intake and its association with nutrition, sleep, and physical activity among schoolchildren in the United Arab Emirates: a national cross-sectional study.

European journal of nutrition·2023
Same author

Reflection on Ramadan Fasting Research Related to Sustainable Development Goal 3 (Good Health and Well-Being): A Bibliometric Analysis.

Journal of religion and health·2023

相关实验视频

Updated: Jul 9, 2025

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
05:49

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

867

使用基于深度学习的卷积神经网络算法对牙科咬牙翼放射图片进行细分.

Talal Bonny1, Abdelaziz Al-Ali2, Mohammed Al-Ali2

  • 1Department of Computer Engineering, University of Sharjah, Sharjah, United Arab Emirates. tbonny@sharjah.ac.ae.

Oral radiology
|December 4, 2023
PubMed
概括

雷斯网-18和雷斯网-50深度学习模型在对牙咬牙翼放射图片进行细分时取得了超过93%的准确性. 本研究确定了最佳分段技术,以改善牙科诊断和治疗规划.

关键词:
咬翅膀的X光学图片深度学习是一种深度学习.开始resnetv2的开始机器学习 机器学习医学科目标题 (MeSH) 医学科目标题移动网络电视2复网-50 复网-50 复网-50 复网-50 复网Xception 接收 接收 接收

更多相关视频

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures
09:10

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Published on: August 5, 2021

1.8K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.8K

相关实验视频

Last Updated: Jul 9, 2025

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
05:49

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

867
Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures
09:10

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Published on: August 5, 2021

1.8K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.8K

科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 计算机视觉 计算机视觉

背景情况:

  • 牙图像细分对于诊断至关重要,但由于图像质量问题而具有挑战性.
  • 深度学习模型在分析复杂的牙图像方面提供了有希望的进步.

研究的目的:

  • 评估和识别最有效的深度学习细分技术,用于咬翼放射.
  • 根据准确性,训练时间和模型复杂性来比较细分性能.

主要方法:

  • 采用了深度学习模型:Resnet-18,Resnet-50,Xception,Inception Resnet v2和Mobilenetv2.2,这些都是使用的深度学习模型.
  • 使用MATLAB进行图像预处理和图形切割细分,以创建二进制面具.
  • 训练和验证的模型分别在298张和99张X射线图上进行了测试,并对99张图像进行了测试.

主要成果:

  • 在Resnet-18和Resnet-50模型中,分别显示了高分段精度,分别为93.67%和94.42%.
  • 性能是根据准确性,速度和模型大小 (参数数量) 进行评估的.
  • 研究结果与以前的研究结果进行了比较,以突出牙科图像细分方面的进展.

结论:

  • Resnet-50和Resnet-18对于分割咬翼射线图非常有效,提供更高的准确性.
  • 这项研究为选择最佳细分方法在实际牙科图像分析中提供了指导.
  • 该研究通过改进图像分析,有助于推进牙科诊断和治疗规划.