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 botnet propagation model in complex networks considering hybrid method for botnet detection.

PloS one·2026
Same author

Development and psychometric evaluation of two substance use disorder knowledge scales.

Drug and alcohol dependence·2026
Same author

A novel framework using particle swarm optimization and long short-term memory networks for stock market forcasting.

Scientific reports·2025
Same author

Enacted Stigma from Family and Drug Use during Opioid Use Disorder Treatment.

Stigma and health·2025
Same author

Ultrasound-intensified wet-heating induced Maillard reaction between hemp cake protein and maltodextrin: Structural characterisation, techno-functional and antioxidant properties.

Ultrasonics sonochemistry·2025
Same author

Predictors of Enacted Stigma Following Disclosure Among People in Recovery From Opioid Use Disorder: A Machine Learning Approach.

Journal of substance use·2025

相关实验视频

Updated: Jun 14, 2025

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

2.7K

一种使用医学图像分类的深度卷积神经网络方法.

Mohammad Mousavi1, Soodeh Hosseini2

  • 1Department of Computer Science, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran.

BMC medical informatics and decision making
|August 29, 2024
PubMed
概括
此摘要是机器生成的。

这项研究介绍了一种使用咳声音和医疗图像的自动COVID-19检测模型. 该模型实现了高精度,有助于快速查和诊断冠状病毒疾病.

关键词:
卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.互联网的健康事物互联网.医学图像分类 医学图像分类

更多相关视频

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.0K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

379

相关实验视频

Last Updated: Jun 14, 2025

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

2.7K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.0K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

379

科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 流行病学 流行病学

背景情况:

  • 像COVID-19这样的流行病的全球快速传播需要早期和准确的诊断.
  • 及时的医疗干预和公共卫生措施取决于有效的疾病检测.

研究的目的:

  • 提出一个自动化的COVID-19检测模型,整合呼吸声分析和医学图像解释.
  • 使用健康物联网 (IoHT) 技术提高COVID-19诊断的速度和准确性.

主要方法:

  • 分析了咳声音,以区分健康人和COVID-19患者,达到94.999%的准确性.
  • 预先训练的卷积神经网络模型 (InceptionResNetV2,InceptionV3,EfficientNetB4) 用于对胸部X射线和CT扫描进行分类.
  • 转移学习用于两个医疗图像数据集,以改进疾病识别.

主要成果:

  • 咳声音分析模型在初始COVID-19查中表现出高效.
  • 在CT扫描图像分类方面,InceptionResNetV2实现了99.414%的准确性.
  • InceptionV3和EfficientNetB4模型在放射学图像分类方面获得了96.943%的准确性.

结论:

  • 拟议的基于IoHT的模型为快速和准确的COVID-19检测提供了一个强大的解决方案.
  • 这种自动化系统支持放射科医生确认诊断,并促进公共卫生查.