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

How Can Large Language Models Improve Health Research Skills: A Perspective Study.

Health science reports·2026
Same author

Evaluation of various traditional machine learning techniques for predicting the acute effect of different hamstring muscle stretching methods among male soccer players.

Scientific reports·2025
Same author

What are the applications of ChatGPT in healthcare: Gain or loss?

Health science reports·2024
Same author

The effect of data balancing approaches on the prediction of metabolic syndrome using non-invasive parameters based on random forest.

BMC bioinformatics·2024
Same author

Metabolic syndrome prediction using non-invasive and dietary parameters based on a support vector machine.

Nutrition, metabolism, and cardiovascular diseases : NMCD·2023
Same author

Evaluating Neck Circumference as an Independent Predictor of Metabolic Syndrome and Its Components Among Adults: A Population-Based Study.

Cureus·2023

相关实验视频

Updated: Jul 2, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

1.8K

AFEX-Net:适应特征提取卷积神经网络用于计算机断层扫描图像的分类.

Roxana Zahedi Nasab1, Hadis Mohseni1, Mahdieh Montazeri2

  • 1Department of Computer Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.

Digital health
|February 26, 2024
PubMed
概括

一个新的深层卷积神经网络,AFEX-Net,有效地从胸部CT扫描中对肺部疾病进行分类. 这种人工智能工具有助于早期检测和诊断,以更快的训练和高准确性超过现有方法.

关键词:
卷积神经网络是一种卷积神经网络.适应性特征提取 适应性特征提取胸部计算机断层扫描图像 图像这是分类分类的分类.

更多相关视频

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K
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 2, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

1.8K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K
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:

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

背景情况:

  • 深度卷积神经网络 (CNN) 对于医疗图像处理至关重要.
  • 像COVID-19这样的新出现的肺部疾病需要先进的诊断工具.
  • 从胸部CT扫描中早期检测肺部疾病是一个重要的研究重点.

研究的目的:

  • 介绍和评估AFEX-Net,一个高效的CNN用于分类肺部疾病.
  • 评估AFEX-Net在CT图像中区分各种肺部指示的能力.
  • 通过医学成像,使肺部疾病的早期诊断成为可能.

主要方法:

  • 设计了AFEX-Net,一个轻量级的CNN,具有可适应的层和功能.
  • 在超过10,000个胸部CT切片 (CC数据集) 上训练并测试了AFEX-Net.
  • 使用公共COVID-CTset数据集和有效的预处理验证了可通用性.

主要成果:

  • 在CC和COVID-CT数据集上,AFEX-Net表现出高准确度.
  • 由于其轻量级的设计,实现了比同类CNN快三倍的学习速度.
  • 成功提取了用于分类肺部疾病的特征,特别是在早期阶段.

结论:

  • AFEX-Net是一个高性能的CNN,用于从胸部CT图像进行肺部疾病分类.
  • 它的效率,适应性和兼容性使其成为早期检测的可靠工具.
  • AFEX-Net支持及时诊断和管理各种肺部疾病.