Jove
Visualize
Contact Us

Related Concept Videos

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

Imaging Studies for Cardiovascular System III: X-Ray

372
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...
372
Radiological Investigation I: X-ray and CT01:30

Radiological Investigation I: X-ray and CT

848
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...
848

You might also read

Related Articles

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

Sort by
Same author

Dysregulation of the Tau-Microtubule-End-Binding Protein Axis in Alzheimer's Disease and Related Tauopathies.

International journal of molecular sciences·2026
Same author

Dynamic Interfacial Design in Adaptive Hybrid Materials Enables Reversible and Tunable Mechano-Optic Smart Responses.

ACS nano·2026
Same author

Genome Wide Identification and Characterization of <i>BrE2F</i> Family Gene of <i>Brassica rapa</i>.

International journal of genomics·2026
Same author

Spatio-temporal distribution patterns and risk assessment of microplastics along a coastal beach in southern Bangladesh.

Marine pollution bulletin·2026
Same author

Disrespect, Abuse, and Satisfaction With Facility-Based Childbirth in Rural Bangladesh.

Health science reports·2026
Same author

CodeStream: A dataset of iterative programming submissions with sequential verdict traces and attempt histories.

Data in brief·2026
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 Experiment Video

Updated: Nov 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

14.5K

CoroDet: A deep learning based classification for COVID-19 detection using chest X-ray images.

Emtiaz Hussain1, Mahmudul Hasan1, Md Anisur Rahman2

  • 1Department of Computer Science and Engineering, Brac University, Dhaka, Bangladesh.

Chaos, Solitons, and Fractals
|November 30, 2020
PubMed
Summary

A new Convolutional Neural Network (CNN) model, CoroDet, accurately detects COVID-19 using chest X-ray and CT scans. This AI-driven approach offers a rapid and reliable alternative to traditional testing, addressing global shortages.

Keywords:
AccuracyCOVID-19Confusion matrixConvolutional neural networkDeep learningPneumonia-bacterialPneumonia-viralX-ray

More Related Videos

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

249
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

2.3K

Related Experiment Videos

Last Updated: Nov 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

14.5K
Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

249
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

2.3K

Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence in Healthcare
  • Radiology

Background:

  • COVID-19, a global pandemic, necessitates rapid and accurate detection methods.
  • Traditional testing kits face shortages, particularly in developing countries.
  • Radiological imaging (X-ray, CT scans) offers valuable diagnostic information for COVID-19.

Purpose of the Study:

  • To develop a novel Convolutional Neural Network (CNN) model named CoroDet for automated COVID-19 detection.
  • To evaluate CoroDet's performance in classifying chest X-ray and CT scan images for COVID-19.
  • To address the scarcity of COVID-19 testing kits through an AI-powered diagnostic tool.

Main Methods:

  • A new CNN model, CoroDet, was designed for automatic COVID-19 detection using raw chest X-ray and CT scan images.
  • CoroDet was trained for 2-class (COVID vs. Normal), 3-class (COVID, Normal, non-COVID pneumonia), and 4-class (COVID, Normal, non-COVID viral pneumonia, non-COVID bacterial pneumonia) classification.
  • The model's performance was benchmarked against ten existing COVID detection techniques.

Main Results:

  • CoroDet achieved high classification accuracies: 99.1% for 2-class, 94.2% for 3-class, and 91.2% for 4-class.
  • The model outperformed existing state-of-the-art methods in COVID-19 detection accuracy.
  • The study utilized the largest dataset of X-ray images for COVID detection to date.

Conclusions:

  • CoroDet demonstrates superior performance compared to existing methods for COVID-19 detection.
  • The model can aid clinicians in making timely and informed decisions for COVID-19 diagnosis.
  • CoroDet offers a potential solution to mitigate the global shortage of COVID-19 testing kits.