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

Automatic Processing and Automatic Social Behavior01:28

Automatic Processing and Automatic Social Behavior

Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...

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

Artificial Intelligence in Neurology and Neuro-Ophthalmology: Current Applications and Future Directions.

Neurologic clinics·2026
Same author

Updates on Treatment in Thyroid Eye Disease for the Neurologist.

Neurologic clinics·2026
Same author

Neuro-Ophthalmology.

Neurologic clinics·2026
Same author

A modular PLC simulation method for virtual replication of a cost-effective industrial automation laboratory.

MethodsX·2026
Same author

Response to: 'Comment on: 'Association between visual impairment and sleep quality: A cross-sectional, comparative study of severity, eye conditions, and risk factors".

Eye (London, England)·2026
Same author

Investigating the landscape of cognitive enhancers use among future physicians: Findings from a developing Middle Eastern country.

Journal of ethnicity in substance abuse·2026

相关实验视频

Updated: May 21, 2026

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition
09:37

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Published on: August 18, 2022

2.5K

高效的堆叠集体学习模型用于自动化角查.

Zahra J Muhsin1, Rami Qahwaji2, Ibrahim Ghafir1

  • 1Faculty of Engineering and Digital Technologies, University of Bradford, Bradford, BD7 1DP, UK.

Eye and vision (London, England)
|June 24, 2025
PubMed
概括

这项研究引入了一种用于自动化角 (KC) 查的新堆叠组合学习方法,达到99.72%的准确性. 该方法通过改善角膜数据的分类来增强早期检测和患者管理.

关键词:
Corneal 断层扫描 角质断层扫描 角质断层扫描 角质断层扫描组合学习学习 组合学习功能选择 功能选择甲状腺查 甲状腺查堆叠集体学习 堆叠集体学习

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

3.0K
Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping
07:11

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Published on: December 8, 2023

1.9K

相关实验视频

Last Updated: May 21, 2026

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition
09:37

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Published on: August 18, 2022

2.5K
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

3.0K
Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping
07:11

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Published on: December 8, 2023

1.9K

科学领域:

  • 眼科医生 眼科 眼科
  • 医疗成像医学成像
  • 机器学习 机器学习

背景情况:

  • (KC) 查严重依赖于传统的机器学习,堆叠组合方法尚未被探索.
  • 这项研究引入了一种新的堆叠集体学习方法,用于增强自动化KC选.

研究的目的:

  • 开发和评估一个堆叠组合学习模型,以改进非KC (NKC),亚临床KC (SCKC) 和临床KC (CKC) 的分类.
  • 通过使用角膜成像数据,提高自动化角膜检测的准确性和效率.

主要方法:

  • 使用了2491个角膜病例 (NKC,SCKC,CKC) 的数据集,其中79个具有Pentacam特征.
  • 实施预处理和特征选择以确定关键指数:最的前点角质量测量,表面偏差,垂直不对称,高度分散和高度不对称.
  • 开发了一个堆叠组合模型,将基于树的分类器 (随机森林,梯度增强,决策树) 与SVM元分类器集成在一起.

主要成果:

  • 功能选择使参数减少了93.67%,培训时间减少了85%以上.
  • 该模型实现了99.72%的准确度,精度,灵敏度,F1和F2得分,MCC为0.995.
  • 在未见的数据上表现出强大的性能,准确地分类所有NKC和CKC病例,具有很高的概括性.

结论:

  • 堆叠组合方法有效地结合了各种模型和Pentacam指数,以实现可靠的KC选.
  • 为临床医生提供了一个自动化工具,用于早期检测角和改善患者管理.