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

Computed Tomography01:10

Computed Tomography

4.5K
Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
4.5K

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

Hybridizing deep learning algorithms and geostatistical approaches for improved crop yield disaggregation.

PloS one·2026
Same author

Lightweight dual-stage feature refinement for black gram leaf disease classification using ConViTSE.

Scientific reports·2025
Same author

A multi-modal AI framework integrating Siamese networks and few-shot learning for early fetal health risk assessment.

MethodsX·2025
Same author

Green synthesis of demethoxycurcumin-loaded chitosan nanoparticles for the management of potato late blight caused by Phytophthora infestans.

International journal of biological macromolecules·2025
Same author

Multiple patho-phenotyping and molecular analysis to characterize wide-spectrum durable leaf rust resistance in wheat collections from India.

Frontiers in microbiology·2025
Same author

Automated severity level estimation of wheat rust using an EfficientNet-CBAM hybrid model.

Frontiers in plant science·2025

相关实验视频

Updated: Jul 6, 2025

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

14.2K

从CT切片中诊断Covid-19使用鱼优化算法,支持向量机和多层感知子.

R Betshrine Rachel1, H Khanna Nehemiah1, Vaibhav Kumar Singh2

  • 1Ramanujan Computing Centre, College of Engineering Guindy, Anna University, Chennai, Tamil Nadu, India.

Journal of X-ray science and technology
|January 8, 2024
PubMed
概括

一个新的计算机辅助诊断 (CAD) 系统有效地从胸部CT扫描中识别Covid-19. 使用鱼优化算法 (WOA) 的特征选择显著提高了多层感知器 (MLP) 的精度,达到88.94%.

关键词:
这就是Covid-19的原因.在MLP中,MLP是MLP.在SVM中,SVM是SVM.,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,肯达尔的相关系数图表.

更多相关视频

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.6K
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 6, 2025

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

14.2K
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.6K
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
  • 医疗保健中的人工智能
  • 诊断系统 诊断系统

背景情况:

  • 新冠病毒病2019 (Covid-19) 是由SARS-CoV-2引起的严重呼吸道疾病.
  • 准确及时诊断对于管理Covid-19至关重要.
  • 胸部计算机断层扫描 (CT) 是Covid-19检测的关键成像方式.

研究的目的:

  • 开发和评估使用胸部CT切片检测Covid-19的计算机辅助诊断 (CAD) 系统.
  • 通过优化功能选择来提高诊断准确性.
  • 将拟议系统的性能与已有的机器学习分类器进行比较.

主要方法:

  • 使用Otsu的值方法进行肺组织细分.
  • 识别和注释COVID-19病变作为感兴趣的区域 (ROI).
  • 特性提取 (纹理和形状),通过鱼优化算法 (WOA) 和支持矢量机 (SVM) 精度进行选择,并使用多层感知器 (MLP) 进行分类.

主要成果:

  • 拟议的CAD系统在特征选择方面实现了88.94%的准确性.
  • 没有特征选择的MLP分类器获得了80.40%的准确性.
  • 该系统在实时数据集上显著超过了八个基准机器学习分类器.

结论:

  • 使用WOA的特征选择大大提高了MLP分类器用于COVID-19检测的诊断准确度.
  • 开发的CAD系统显示出高效率和临床应用潜力,用于从胸部CT扫描中诊断Covid-19.
  • 统计分析证实了拟议方法对所考虑数据集的重大影响和优势.