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

Cancer Survival Analysis01:21

Cancer Survival Analysis

315
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
315

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

Analysis of microstructural thermo-mechanical and tribological properties of polymer composite developed with hybrid natural fibers.

Scientific reports·2026
Same author

Transforming ceramic residues into circular economy solutions in geotechnical engineering.

Scientific reports·2026
Same author

A hybrid RSM-Spherical Fuzzy AHP-Fuzzy VIKOR approach for optimizing drilling of AM60 magnesium alloy using biodegradable MQL.

Scientific reports·2026
Same author

Attention-based Deep Feature Fusion for Automated Dysarthria Severity Classification: A Speech-based Computational Functional Marker Relevant to Neurovascular and Neurodegenerative Conditions.

Current neurovascular research·2026
Same author

Taguchi-based multi-response statistical optimization and performance assessment of high-strength concrete incorporating weathered crystalline rock fine aggregate.

Scientific reports·2026
Same author

Fracture behavior and interfacial toughening mechanisms of SiC microfiller-reinforced gossypium/glass-epoxy hybrid composites for automotive structural applications.

Scientific reports·2026

相关实验视频

Updated: May 22, 2025

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
07:53

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

1.3K

使用具有多属性决策系统和深度学习模型的机器学习分类器检测肺癌.

T Meeradevi1, S Sasikala1, L Murali2

  • 1Department of ECE, Kongu Engineering College, Erode, Tamil Nadu, India.

Scientific reports
|March 13, 2025
PubMed
概括

这项研究利用机器学习 (ML) 和X射线图像上的深度学习 (DL) 来增强肺部疾病的检测. 深度学习模型实现了97.05%的准确性,超过了ML方法来分类良性或恶性肺部疾病.

关键词:
深度学习是一种深度学习.肺部疾病是一种肺部疾病.机器学习 机器学习这里是TOPSIS的地图.

更多相关视频

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.6K
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

相关实验视频

Last Updated: May 22, 2025

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
07:53

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

1.3K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.6K
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

科学领域:

  • 医学成像分析 医学成像分析
  • 医疗保健中的人工智能
  • 呼吸系统医学 呼吸系统医学

背景情况:

  • 慢性呼吸道疾病源于呼吸道和肺部疾病,通常是由烟草烟雾,环境污染物和儿童感染引起的.
  • 通过医学图像分析早期检测肺部疾病对于有效的患者治疗至关重要.
  • 分类肺部X射线图像和识别特定疾病,如Atelectasis,透,结节和肺炎,有助于诊断.

研究的目的:

  • 为了将肺部X射线图像分类为良性或恶性.
  • 为了识别特定的恶性肺部疾病,包括Atelectasis,透,结节和肺炎.
  • 为了比较机器学习 (ML) 和深度学习 (DL) 模型对肺部疾病分类的有效性.

主要方法:

  • 利用机器学习 (ML) 方法与顺序偏好与理想解决方案相似的技术 (TOPSIS) 结合起来,以对分类器进行排名.
  • 提出了深度学习 (DL) 模型Inception v3用于肺部X射线图像分析.
  • 评价和排名支持向量机 (SVM) 带有辐射基函数 (RBF) 作为一流的ML分类器.

主要成果:

  • 在评估的ML方法中,带有辐射基函数 (RBF) 的支向量机 (SVM) 被确定为最佳分类器.
  • 深度学习 (DL) 模型Inception v3实现了97.05%的卓越精度.
  • 与相同数据集上的ML方法相比,DL方法的准确性提高了11.8%.

结论:

  • 深度学习模型,特别是Inception v3,与传统的ML方法相比,在X射线图像上对肺部疾病的分类提供了显著更高的准确性.
  • 整合ML和DL技术为早期和准确检测各种肺部疾病提供了一个强大的框架.
  • 肺部X射线的准确分类有助于及时诊断和治疗计划的慢性呼吸道疾病患者.