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

Pulmonary Tuberculosis IV01:26

Pulmonary Tuberculosis IV

Tuberculosis, more commonly referred to as TB, is an infectious disease stemming from Mycobacterium tuberculosis. While it primarily impacts the lungs, TB can also affect other body areas. Given its severity and global impact, timely and accurate diagnosis is crucial for controlling its spread and improving patient outcomes.
Several diagnostic approaches are used to detect TB. The conventional method is the Tuberculin Skin Test (TST), also known as the Mantoux test. However, this method has...

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

Lightweight deep learning for medical imaging using MobileNetV2-based brain pathology classification with Grad-CAM interpretability.

Frontiers in medicine·2026
Same author

CT-Malaria Detection via Adaptive-Weighted Deep Learning Models.

Biomedicines·2026
Same author

Correction: Almadhor et al. Efficient Feature-Selection-Based Stacking Model for Stress Detection Based on Chest Electrodermal Activity. <i>Sensors</i> 2023, <i>23</i>, 6664.

Sensors (Basel, Switzerland)·2025
Same author

Digital twin based deep learning framework for personalized thermal comfort prediction and energy efficient operation in smart buildings.

Scientific reports·2025
Same author

SS-DRPL: self-supervised deep representation pattern learning for voice-based Parkinson's disease detection.

Frontiers in computational neuroscience·2024
Same author

Survey on Blockchain-Based Data Storage Security for Android Mobile Applications.

Sensors (Basel, Switzerland)·2023

相关实验视频

Updated: May 26, 2026

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
08:05

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

14.1K

TSSG-CNN:使用自适应卷积神经网络检测和诊断结核病语义细分引导模型.

Tae Hoon Kim1, Moez Krichen2, Stephen Ojo3

  • 1School of Information and Electronic Engineering, Zhejiang University of Science and Technology, No. 318, Hangzhou 310023, China.

Diagnostics (Basel, Switzerland)
|June 19, 2024
PubMed
概括
此摘要是机器生成的。

一种新的结核细分指导诊断模型 (TSSG-CNN) 在从X射线图像中检测结核病时达到98.75%的准确性. 这种深度学习方法精确地细分和诊断结核病,在早期检测方面取得了重大进展.

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.医疗保健 医疗保健 医疗保健 医疗保健细分模型的细分模型.结核病是一种肺结核病.

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

2.7K
Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

2.4K

相关实验视频

Last Updated: May 26, 2026

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
08:05

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

14.1K
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
Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

2.4K

科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 计算机科学 计算机科学

背景情况:

  • 结核病 (TB) 是一种由Mycobacterium引起的传染性传染病,主要影响肺部,但可能影响其他器官.
  • 结核病通过感染个体的空气中滴滴传播,需要有效的诊断工具.
  • 目前的诊断方法需要改进,以提高准确性和早期检测.

研究的目的:

  • 开发和评估一种新的深度学习模型,用于使用胸部X射线图像进行增强的结核病检测.
  • 通过将语义细分与卷积神经网络 (CNN) 架构相结合,提高结核病诊断的精度.
  • 评估拟议的结核细分指导诊断模型 (TSSG-CNN) 与其他既有模型的性能.

主要方法:

  • 提出了一种新的结核细分指导诊断模型 (TSSG-CNN),集成语义细分和自适应CNN.
  • 利用结合深度学习细分和分类模型进行精确的胸部X射线图像分析.
  • 采用Mayfly算法 (MA) 进行简化功能优化,并将TSSG-CNN与简单的CNN,BN-CNN和DCNN进行比较.

主要成果:

  • TSSG-CNN模型实现了98.75%的高精度和98.70%的F1得分.
  • 拟议的模型显著优于其他评估的模型,包括简单的CNN,BN-CNN和DCNN.
  • 评估结果证实了深度学习细分模型在TSSG-CNN架构中的有效性.

结论:

  • TSSG-CNN模型代表了一种高度准确的结核病检测策略.
  • 该研究强调了TSSG-CNN模型在精确和早期诊断结核病方面的潜力.
  • 需要进一步的研究来探索这种深度学习方法在医学诊断中的全部功能.