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

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 X-rays, and by 1900, X-ray was widely...
Imaging Studies for Cardiovascular System III: X-Ray01:20

Imaging Studies for Cardiovascular System III: X-Ray

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...

You might also read

Related Articles

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

Sort by
Same author

A Robust Automated Analog Circuits Classification Involving a Graph Neural Network and a Novel Data Augmentation Strategy.

Sensors (Basel, Switzerland)·2023
Same author

Data Augmentation for a Virtual-Sensor-Based Nitrogen and Phosphorus Monitoring.

Sensors (Basel, Switzerland)·2023
Same author

Seismic Waveform Inversion Capability on Resource-Constrained Edge Devices.

Journal of imaging·2022
Same author

A Novel Zernike Moment-Based Real-Time Head Pose and Gaze Estimation Framework for Accuracy-Sensitive Applications.

Sensors (Basel, Switzerland)·2022
Same author

A Virtual Sensing Concept for Nitrogen and Phosphorus Monitoring Using Machine Learning Techniques.

Sensors (Basel, Switzerland)·2022
Same author

A Smart Visual Sensing Concept Involving Deep Learning for a Robust Optical Character Recognition under Hard Real-World Conditions.

Sensors (Basel, Switzerland)·2022
Same journal

Human-AI Interaction in Interventional Radiology: A Narrative Review of Current Applications, Challenges, and Future Directions.

Journal of imaging·2026
Same journal

Coronary Artery Anomalies and Anatomical Variants: Cross-Sectional Diagnostic Imaging and Clinical Background.

Journal of imaging·2026
Same journal

YoLeTooth: A Unified Framework for Joint Tooth Segmentation and Periapical Lesion Detection in Panoramic Radiographs.

Journal of imaging·2026
Same journal

Radiomics-Guided Multi-Sequence Learning for Pathological Complete Response Prediction from Breast MRI with Missing Auxiliary Sequences.

Journal of imaging·2026
Same journal

Cutaneous Thermography in Arthropathies: Quantitative Imaging, Machine Learning, and Clinical Translation.

Journal of imaging·2026
Same journal

Two-Stage Dynamic Synergistic Segmentation Method for Myocardial Pathology.

Journal of imaging·2026
See all related articles

Related Experiment Video

Updated: Jun 29, 2026

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

Enhancing COVID-19 Detection: An Xception-Based Model with Advanced Transfer Learning from X-ray Thorax Images.

Reagan E Mandiya1,2, Hervé M Kongo1, Selain K Kasereka1,2

  • 1Mathematics, Statistics and Computer Science Department, University of Kinshasa, Kinshasa XI P.O. Box 190, Democratic Republic of the Congo.

Journal of Imaging
|March 27, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an advanced Xception model with transfer learning for detecting Coronavirus Disease 2019 (COVID-19) from chest X-rays. The new method significantly improves diagnostic accuracy compared to existing models.

Keywords:
COVID-19X-ray imagesXceptionmedical imagingneural networktransfer learning

More Related Videos

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

2.7K
DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

744

Related Experiment Videos

Last Updated: Jun 29, 2026

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
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

2.7K
DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

744

Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Infectious Disease Diagnostics

Background:

  • Accurate and rapid Coronavirus Disease 2019 (COVID-19) detection is critical for patient management and pandemic control.
  • Existing deep learning models, particularly Convolutional Neural Networks (CNNs), face challenges like overfitting and high computational costs in medical image analysis.
  • There is a need for more efficient and accurate AI models for COVID-19 diagnosis using radiological images.

Purpose of the Study:

  • To develop and evaluate an innovative deep learning model for enhanced COVID-19 detection from chest X-ray images.
  • To address the limitations of existing models, including expressiveness issues and resource-intensive training.
  • To improve the accuracy and efficiency of AI-driven COVID-19 diagnostic tools.

Main Methods:

  • Utilized the Xception architecture, a state-of-the-art CNN, for image classification.
  • Incorporated advanced transfer learning techniques to augment the Xception model's performance.
  • Trained and validated the model on a dataset of chest X-ray images for COVID-19 identification.

Main Results:

  • The proposed Xception model with transfer learning demonstrated superior predictive accuracy compared to baseline models like VGG-16 and ResNet.
  • The model effectively identified COVID-19 cases from chest X-ray images, outperforming established methods.
  • Experimental results indicate enhanced diagnostic performance and potential for overcoming common deep learning challenges.

Conclusions:

  • The developed transfer learning-enhanced Xception model offers a significant advancement in AI-based COVID-19 detection from X-rays.
  • This approach represents a promising stride towards more accurate, efficient, and accessible diagnostic tools for infectious diseases.
  • The findings suggest a viable solution for improving patient outcomes and public health surveillance during pandemics.