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

Radiological Investigation I: X-ray and CT01:30

Radiological Investigation I: X-ray and CT

319
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...
319
Computed Tomography01:10

Computed Tomography

4.7K
Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
4.7K
Imaging Studies for Cardiovascular System III: X-Ray01:20

Imaging Studies for Cardiovascular System III: X-Ray

224
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...
224
Imaging Studies for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

53
Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...
53
X-ray Imaging01:24

X-ray Imaging

5.7K
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...
5.7K
Positron Emission Tomography01:29

Positron Emission Tomography

4.4K
Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
One of the main requirements of a PET scan is a positron-emitting radioisotope, which is produced in a cyclotron and then attached to a substance used by the part of the body...
4.4K

You might also read

Related Articles

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

Sort by
Same author

Chemical contaminants and fish: implications for health and ecosystem sustainability.

Environmental monitoring and assessment·2026
Same author

LightAttn-YOLO-V8: an efficient colorectal polyp detection with lightweight attention mechanism based on YOLO.

Scientific reports·2026
Same author

Enhancing Hypertension Risk Diagnosis Using a Hybrid Machine Learning Framework: Leveraging Body Composition Data.

BioMed research international·2026
Same author

Complex emotion recognition system using basic emotions via facial expression, electroencephalogram, and electrocardiogram signals: a review.

Frontiers in psychology·2026
Same author

Bridging modalities with AI: a review of AI advances in multimodal biomedical imaging.

Communications engineering·2026
Same author

Enhancing Hypertension Risk Diagnosis Using a Hybrid Machine Learning Framework: Leveraging Body Composition Data.

BioMed research international·2026

Related Experiment Video

Updated: Aug 6, 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

14.3K

CCTCOVID: COVID-19 detection from chest X-ray images using Compact Convolutional Transformers.

Abdolreza Marefat1, Mahdieh Marefat2, Javad Hassannataj Joloudari3

  • 1Department of Computer Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran.

Frontiers in Public Health
|March 16, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a new deep learning method using Compact Convolutional Transformers (CCT) for fast and accurate COVID-19 detection from X-ray images. The transformer-based approach achieved 99.22% accuracy, significantly aiding in rapid community screening.

Keywords:
COVID-19Compact Convolutional TransformersConvolutional Neural Networksdeep learningvision transformers

More Related Videos

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

1.9K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

471

Related Experiment Videos

Last Updated: Aug 6, 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

14.3K
A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

1.9K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

471

Area of Science:

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

Background:

  • The rapid person-to-person transmissibility of COVID-19 necessitates urgent and accurate diagnostic methods.
  • Screening medical images like X-rays is crucial for COVID-19 detection but faces challenges due to high case numbers and complex procedures.
  • Deep learning shows significant promise for automating complex medical tasks, including image analysis.

Purpose of the Study:

  • To develop and evaluate a novel transformer-based deep learning method for automated COVID-19 detection.
  • To utilize Compact Convolutional Transformers (CCT) for analyzing X-ray images for COVID-19 diagnosis.
  • To improve the efficiency and accuracy of COVID-19 screening in clinical settings.

Main Methods:

  • Implementation of a transformer-based deep learning model utilizing Compact Convolutional Transformers (CCT).
  • Training and validation of the CCT model on a dataset of X-ray images for COVID-19 classification.
  • Comparative analysis against existing methods to demonstrate performance efficacy.

Main Results:

  • The proposed CCT-based method achieved a high accuracy of 99.22% in detecting COVID-19 from X-ray images.
  • The model demonstrated superior performance compared to previous state-of-the-art approaches.
  • The results highlight the effectiveness of transformer architectures in medical image analysis for infectious diseases.

Conclusions:

  • The developed transformer-based deep learning method offers a highly accurate and efficient solution for COVID-19 detection using X-ray images.
  • Compact Convolutional Transformers show significant potential for advancing automated diagnostic tools in radiology.
  • This approach can help alleviate the burden on medical practitioners and improve community-level screening capabilities.