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

328
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...
328
Radiological Investigation I: X-ray and CT01:30

Radiological Investigation I: X-ray and CT

597
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...
597

You might also read

Related Articles

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

Sort by
Same author

Phytochemicals from the leaves of <i>Staphylea arguta</i> (Turpiniae Folium) and their anti-inflammatory activity.

Natural product research·2026
Same author

Deep learning-guided ligand generation for the strigolactone receptor ShHTL7.

Computational biology and chemistry·2026
Same author

Drug-coated balloons vs. drug-eluting stents for coronary artery disease: an updated systematic review and meta-analysis of randomized controlled trials with lesion-specific insights.

Frontiers in cardiovascular medicine·2026
Same author

Mixture Pulsation Model-Based Decision-Making for Resource-Efficient Scheduling in Large-Scale Assembly Lines.

IEEE transactions on cybernetics·2026
Same author

Cinnamic acid-butenedioic lactone derivatives as crop protection agents: synthesis, bioactivity, and molecular docking studies.

Pest management science·2026
Same author

Effects of TiO<sub>2</sub> Nanoparticle Doping on the Micro-Arc Oxidation Coating Structure and Corrosion Resistance of 6061 Aluminum Alloy.

Molecules (Basel, Switzerland)·2026

Related Experiment Video

Updated: Oct 28, 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

14.4K

Correcting data imbalance for semi-supervised COVID-19 detection using X-ray chest images.

Saul Calderon-Ramirez1,2, Shengxiang Yang1, Armaghan Moemeni3

  • 1Centre for Computational Intelligence (CCI), De Montfort University, United Kingdom.

Applied Soft Computing
|July 19, 2021
PubMed
Summary

This study addresses deep learning for COVID-19 detection using chest X-rays with limited data. A novel re-weighting method improves accuracy by up to 18% on imbalanced datasets.

Keywords:
COVID-19Computer aided diagnosisCoronavirusData imbalanceSemi-supervised learning

More Related Videos

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
02:09

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

761
Author Spotlight: Enhancing Diagnostic Strategies and Biomarker Development for Comprehensive Lung Function Analysis
05:56

Author Spotlight: Enhancing Diagnostic Strategies and Biomarker Development for Comprehensive Lung Function Analysis

Published on: August 9, 2024

2.0K

Related Experiment Videos

Last Updated: Oct 28, 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

14.4K
Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
02:09

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

761
Author Spotlight: Enhancing Diagnostic Strategies and Biomarker Development for Comprehensive Lung Function Analysis
05:56

Author Spotlight: Enhancing Diagnostic Strategies and Biomarker Development for Comprehensive Lung Function Analysis

Published on: August 9, 2024

2.0K

Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Computational Biology

Background:

  • Early identification of coronavirus disease (COVID-19) carriers is crucial for disease control.
  • Deep learning on chest X-rays offers a potential pre-diagnostic tool, but requires large, balanced datasets.
  • New outbreaks present challenges with small, imbalanced datasets, hindering deep learning model performance.

Purpose of the Study:

  • To evaluate the MixMatch semi-supervised deep learning architecture with limited and imbalanced chest X-ray datasets for COVID-19 detection.
  • To address the challenge of data imbalance in deep learning for novel viral diseases.
  • To propose and validate a simple data imbalance correction method for improved classification accuracy.

Main Methods:

  • Utilized the MixMatch semi-supervised learning architecture.
  • Evaluated model performance on highly imbalanced datasets with very few labeled observations.
  • Proposed a re-weighting strategy within the loss function to correct for data imbalance, assigning higher weights to under-represented classes.
  • Used pseudo and augmented labels for unlabeled data to determine appropriate weights.

Main Results:

  • Demonstrated the significant negative impact of data imbalance on deep learning model accuracy for COVID-19 detection.
  • The proposed re-weighting method improved classification accuracy by up to 18% compared to the standard MixMatch algorithm.
  • Successfully tested the approach on binary (COVID-19 vs. normal) and multi-class (COVID-19, pneumonia, normal) datasets, including a new dataset from Costa Rican patients.

Conclusions:

  • Semi-supervised learning with data imbalance correction is effective for COVID-19 detection using chest X-rays, even with limited data.
  • The proposed re-weighting technique offers a simple yet powerful solution to improve deep learning model performance in resource-constrained scenarios.
  • This approach holds promise for rapid pre-diagnostic tool development during emerging viral outbreaks.