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

X-ray Imaging01:24

X-ray Imaging

8.6K
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...
8.6K
Imaging Studies for Cardiovascular System III: X-Ray01:20

Imaging Studies for Cardiovascular System III: X-Ray

319
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...
319
Radiological Investigation I: X-ray and CT01:30

Radiological Investigation I: X-ray and CT

488
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...
488
X-ray Diffraction of Biological Samples01:10

X-ray Diffraction of Biological Samples

4.2K
X-ray diffraction or XRD is an analytical tool that utilizes X-rays to study ordered structures such as crystalline organic and inorganic samples, polycrystalline materials, proteins, carbohydrates, and drugs.
According to Bragg's law, when X-rays strike the sample positioned on a stage, the rays are  scattered by the electron clouds around the sample atoms. The  X-ray diffraction or scattering is caused by constructive interference of the X-ray waves that reflect off the internal...
4.2K

You might also read

Related Articles

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

Sort by
Same author

Augmenting Performance Status: A Preliminary Study of Objective Kinematic Assessment Using Motion Capture.

Oncology and therapy·2026
Same author

Lung transplant outcomes in recipients waitlisted for both single and double lung transplantation.

JHLT open·2026
Same author

Mechanism-Stratified Complications After Operative Management of Low-Grade Colon Injuries.

The Journal of surgical research·2026
Same author

Blunt trauma induces a proinvasive transcriptional program in isolated circulating human neutrophils.

The journal of trauma and acute care surgery·2026
Same author

Taking the long view in traumatic peripheral vascular injury repair: An analysis of the PROspective Observational Vascular Injury Treatment postdischarge follow-up registry.

The journal of trauma and acute care surgery·2025
Same author

Primary repair versus resection for American Association for the Surgery of Trauma grades I and II colon injuries: Does the management approach really matter? An Eastern Association for the Surgery of Trauma multicenter trial.

The journal of trauma and acute care surgery·2025

Related Experiment Video

Updated: Oct 15, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.8K

CovXmlc: High performance COVID-19 detection on X-ray images using Multi-Model classification.

Sourabh Singh Verma1, Ajay Prasad2, Anil Kumar3

  • 1SCIT, Manipal University Jaipur, India.

Biomedical Signal Processing and Control
|October 25, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces CovXmlc, a deep learning model using X-ray images for rapid COVID-19 detection. CovXmlc achieved 95% accuracy, outperforming existing methods for faster diagnosis.

Keywords:
COVID-19Chest X-ray imagesConvolutional neural networkCoronavirusDeep learningSARS Cov-2

More Related Videos

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
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.7K

Related Experiment Videos

Last Updated: Oct 15, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.8K
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
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.7K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Biology

Background:

  • The COVID-19 pandemic necessitates rapid diagnostic tools.
  • Current RT-PCR testing is time-consuming, expensive, and can lack accuracy.
  • Radiological imaging, particularly X-rays, shows potential for COVID-19 detection.

Purpose of the Study:

  • To develop and evaluate a novel deep learning model for rapid and accurate COVID-19 detection using X-ray images.
  • To improve upon existing diagnostic methods by leveraging advanced classification techniques.
  • To classify X-ray images into COVID-19 positive and normal categories.

Main Methods:

  • A multi-model classification approach integrating VGG16 Convolution network with Support Vector Machine (SVM).
  • Introduction of additional convolution, pool, and dense layers for enhanced synchronization between VGG16 and SVM.
  • Utilization of the Radial Basis Function for data transformations and optimization.

Main Results:

  • The proposed CovXmlc model achieved a high accuracy of 95% on a minimal dataset.
  • CovXmlc demonstrated superior performance compared to five existing models across various metrics.
  • The model showed significant improvements in recall, precision, and f-score.

Conclusions:

  • CovXmlc offers a promising, accurate, and rapid method for COVID-19 diagnosis using X-ray imaging.
  • The integrated deep learning approach effectively classifies COVID-19 cases, potentially aiding in global health responses.
  • This method provides a viable alternative or supplement to traditional diagnostic techniques.