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

X-ray Imaging01:24

X-ray Imaging

5.3K
German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with...
5.3K

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

Retraction notice to "Persistent organic pollutants in water resources: Fate, occurrence, characterization and risk analysis" [Sci. Total Environ. 831 (2022) 154808].

The Science of the total environment·2026
Same author

Fault-tolerance amplification in line graphs: a framework for resilient PMU placement in power networks.

Scientific reports·2026
Same author

Climate-induced shifts in habitat suitability of forest types and adaptation strategies in the Western Ghats of Tamil Nadu, India.

Scientific reports·2026
Same author

Circular economy through integrated industrial ecology: Innovations in resource recovery and process re-design.

Biotechnology notes (Amsterdam, Netherlands)·2025
Same author

Hybrid deep learning model for spinal tumor diagnosis on MRI scans.

Technology and health care : official journal of the European Society for Engineering and Medicine·2025
Same author

Deep learning for text summarization using NLP for automated news digest.

Scientific reports·2025

相关实验视频

Updated: May 27, 2025

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

640

使用新的深度学习框架在X射线图像中进行有效的COVID-19分类.

P Thilagavathi1, R Geetha2, S Jothi Shri3

  • 1Department of Computer Science and Engineering, Aarupadai Veedu Institute of Technology, Vinayaka Mission & Research Foundation(DU) Paiyanoor, Chennai, Tamil Nadu, India.

Journal of X-ray science and technology
|February 20, 2025
PubMed
概括

这项研究引入了一种新的混合深度学习方法,用于诊断肺部疾病,包括COVID-19,使用人工智能 (AI) 对胸部X射线图像. 该方法实现了高分类准确性,优于现有的快速可靠的疾病识别技术.

关键词:
适应性优化适应性优化在 COVID-19 疫情中,多头注意力注意力注意力斯帕尔斯自动编码器适应性过是一种自适应性过.特性提取过程 特性提取过程混合深度学习是混合深度学习.损失功能的优化优化损失功能的优化.优化了功能,优化了功能.

更多相关视频

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

2.6K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.3K

相关实验视频

Last Updated: May 27, 2025

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

640
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

2.6K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.3K

科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 计算机辅助诊断 计算机辅助诊断

背景情况:

  • 全球越来越关注与肺相关的疾病,COVID-19传播加剧了这一问题.
  • 人工智能 (AI) 可以通过胸部X射线快速识别COVID-19.

研究的目的:

  • 开发和评估一种混合深度学习方法,使用胸部X射线图像来诊断肺部疾病.
  • 通过先进的人工智能技术提高疾病检测的准确性和效率.

主要方法:

  • 利用了一个公共的COVID-19胸部X射线数据集.
  • 预处理的图像使用了改进的无otropic 扩散过 (IADF).
  • 使用的特征提取方法:GLCM,uLBP,HoG,hvnLBP.
  • 使用自适应爬行动物搜索优化 (ARSO) 优化功能选择.
  • 开发了一种基于多头注意力的双向门式循环单元,配备了深度零散自动编码网络 (MhA-Bi-GRU与DSAN) 进行分类.
  • 应用动态征收-飞行优化 (DLF-CO) 以尽量减少损失函数.

主要成果:

  • 实现了高分类准确度:0.95%的0.001学习率和0.98%的0.0001学习率.
  • 与现有方法相比,拟议的方法在各种参数上表现出优越的性能.
  • 使用胸部X射线图像进行肺部疾病的有效诊断得到了证实.

结论:

  • 拟议的混合深度学习方法,集成先进的特征提取和最佳选择,有效地通过X射线图像诊断肺部疾病.
  • 这种人工智能驱动的方法显示了提高医学诊断的准确性和速度的巨大潜力.