Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Imaging Studies for Cardiovascular System III: X-Ray01:20

Imaging Studies for Cardiovascular System III: X-Ray

188
The most common cardiovascular diagnostic test is an X-ray. It produces images of the heart, blood vessels, and adjacent structures.
Definition and Purpose
An X-ray, or radiograph, is a non-invasive method that uses ionizing radiation to take images of internal structures. It is mainly used in cardiac imaging to examine the heart, lungs, and major blood vessels, aiming to identify abnormalities in the heart's size, shape, and position, such as heart failure, congenital defects, and vascular...
188
Radiological Investigation I: X-ray and CT01:30

Radiological Investigation I: X-ray and CT

237
Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and...
237
X-ray Imaging01:24

X-ray Imaging

5.5K
German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with...
5.5K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

An automated quantitative report for multiple sclerosis using only 3D T2-fluid-attenuated inversion recovery MRI.

Neuroradiology·2026
Same author

Structure-function multilayer network integration and cognition in multiple sclerosis.

Network neuroscience (Cambridge, Mass.)·2026
Same author

Advancing Age Modulates Associations Between Cognitive Impairment and Brain Volumes in Early MS.

Annals of clinical and translational neurology·2026
Same author

Brain Age Estimation on T2-FLAIR Scans for Application to Multiple Sclerosis.

Human brain mapping·2026
Same author

Cognitive outcomes in multiple sclerosis are shaped by divergent functional connectivity trajectories.

Brain communications·2026
Same author

SynSpine: an automated workflow for the generation of longitudinal spinal cord synthetic MRI data.

Frontiers in neuroinformatics·2026

Related Experiment Video

Updated: Jul 1, 2025

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
10:26

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

Published on: May 19, 2023

1.8K

Generalizable disease detection using model ensemble on chest X-ray images.

Maider Abad1, Jordi Casas-Roma2,3,4, Ferran Prados2,5,6

  • 1Universitat Oberta de Catalunya, e-Health Center, Barcelona, Spain. mabdvz@uoc.edu.

Scientific Reports
|March 12, 2024
PubMed
Summary

This study evaluated pre-trained convolutional neural network (CNN) models for COVID-19 detection using chest X-rays. An ensemble method significantly improved accuracy on diverse datasets, outperforming individual models.

Keywords:
Domain adaptationEnsemble classifierPre-trained modelsTransfer learningX-ray imaging

More Related Videos

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

1.4K
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

Related Experiment Videos

Last Updated: Jul 1, 2025

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
10:26

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

Published on: May 19, 2023

1.8K
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

1.4K
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

Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Diagnostic Tools

Background:

  • Increasing demand for rapid and accurate healthcare diagnostics.
  • Need for robust AI models in medical image analysis.

Purpose of the Study:

  • To analyze the performance of pre-trained convolutional neural network (CNN) architectures (ResNet50, DenseNet121, Inception-ResNet-v2) for COVID-19 detection from chest X-rays.
  • To evaluate model generalizability across diverse datasets.
  • To develop an ensemble method to enhance diagnostic accuracy.

Main Methods:

  • Curated a large-scale dataset of chest X-ray images (COVID-19 positive and negative cases).
  • Performed internal and external validation of ResNet50, DenseNet121, and Inception-ResNet-v2.
  • Developed an uncertainty-based ensemble method using entropy for model weighting.

Main Results:

  • Individual CNN models showed significant accuracy drops on external validation datasets.
  • DenseNet121 achieved 96.71% accuracy (internal), Inception-ResNet-v2 achieved 76.70% (external).
  • The ensemble method improved accuracy to 97.38% (internal) and 81.18% (external).

Conclusions:

  • Pre-trained CNNs exhibit reduced efficacy on external datasets.
  • An uncertainty-based ensemble approach effectively enhances diagnostic performance and generalizability.
  • The ensemble method offers a promising solution for reliable AI-powered COVID-19 detection.