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

389
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...
389

You might also read

Related Articles

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

Sort by
Same author

Request and reporting models for computed tomography in the multidisciplinary management of cancer patients: consensus between the Italian Society of Medical and Interventional Radiology (SIRM) and the Italian Society of Medical Oncology (AIOM).

La Radiologia medica·2026
Same author

A Method for Workout Video Classification via Explainable and Federated Learning.

Bioengineering (Basel, Switzerland)·2026
Same author

Pancreatic cystic lesions: position paper of the SIRM-AISP multidisciplinary group.

La Radiologia medica·2026
Same author

Sentinel lymph node mapping in gynecologic oncology: technical tips and common pitfalls.

International journal of gynecological cancer : official journal of the International Gynecological Cancer Society·2026
Same author

Total Endovenous Laser Ablation Multicenter (TOTEM) trial: Early results.

Journal of vascular surgery. Venous and lymphatic disorders·2026
Same author

On Vision Transformer Explainability for Personal Protective Equipment Detection: A Qualitative and Quantitative Analysis.

Journal of imaging·2026

Related Experiment Video

Updated: Dec 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

14.6K

Machine learning for coronavirus covid-19 detection from chest x-rays.

Luca Brunese1, Fabio Martinelli2, Francesco Mercaldo1,2

  • 1Department of Medicine and Health Sciences "Vincenzo Tiberio", University of Molise, Campobasso, Italy.

Procedia Computer Science
|October 12, 2020
PubMed
Summary

This study introduces a machine learning method for automatically detecting COVID-19 from chest X-rays. The approach effectively distinguishes COVID-19 from other lung diseases using a dataset of 85 X-ray images.

Keywords:
COVID-19Coronavirusartificial intelligencemachine learningmedical imagesx-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

286
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

743

Related Experiment Videos

Last Updated: Dec 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

14.6K
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

286
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

743

Area of Science:

  • Medical Imaging Analysis
  • Machine Learning in Healthcare
  • Infectious Disease Diagnostics

Background:

  • The global spread of Coronavirus Disease 2019 (COVID-19) necessitates rapid diagnostic tools.
  • Accurate and timely screening of pulmonary diseases, including COVID-19, is crucial for patient management.
  • Traditional diagnostic methods can be time-consuming, highlighting the need for automated solutions.

Purpose of the Study:

  • To develop and evaluate a supervised machine learning method for the automated detection of COVID-19.
  • To assess the efficacy of the proposed method in differentiating COVID-19 from other pulmonary conditions using medical images.
  • To provide a tool for rapid screening of patients suspected of having COVID-19.

Main Methods:

  • Utilized supervised machine learning techniques to build a predictive model.
  • Employed a dataset comprising 85 chest X-ray images, made available for research purposes.
  • Trained and validated the model on the chest X-ray data to identify patterns indicative of COVID-19.

Main Results:

  • The developed machine learning model demonstrated effectiveness in identifying COVID-19.
  • The method successfully discriminated between COVID-19 and other pulmonary diseases present in the dataset.
  • Experimental results confirm the potential of the proposed approach for automated disease detection.

Conclusions:

  • The proposed machine learning method shows promise for the automated detection of COVID-19 using chest X-rays.
  • This approach can aid in the rapid screening and diagnosis of COVID-19, complementing existing methods.
  • Further research with larger datasets could enhance the generalizability and clinical utility of this technique.