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

214
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...
214
Imaging Studies for Cardiovascular System III: X-Ray01:20

Imaging Studies for Cardiovascular System III: X-Ray

145
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...
145
X-ray Imaging01:24

X-ray Imaging

5.4K
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...
5.4K

You might also read

Related Articles

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

Sort by
Same author

Non-Invasive Blood Glucose Estimation System Based on a Neural Network with Dual-Wavelength Photoplethysmography and Bioelectrical Impedance Measuring.

Sensors (Basel, Switzerland)·2022
Same author

Feature Fusion of a Deep-Learning Algorithm into Wearable Sensor Devices for Human Activity Recognition.

Sensors (Basel, Switzerland)·2021
Same author

Optical Design with Narrow-Band Imaging for a Capsule Endoscope.

Journal of healthcare engineering·2018
Same author

Aspherical Lens Design Using Genetic Algorithm for Reducing Aberrations in Multifocal Artificial Intraocular Lens.

Materials (Basel, Switzerland)·2017
Same author

A Study of Dispersion Compensation of Polarization Multiplexing-Based OFDM-OCDMA for Radio-over-Fiber Transmissions.

Sensors (Basel, Switzerland)·2016
Same author

Study of optical design of three-dimensional digital ophthalmoscopes.

Applied optics·2015

Related Experiment Video

Updated: Jun 6, 2025

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

1.7K

Lightweight convolutional neural network for chest X-ray images classification.

Chih-Ta Yen1, Chia-Yu Tsao2

  • 1Department of Electrical Engineering, National Taiwan Ocean University, Keelung City, 202301, Taiwan. chihtayen@gmail.com.

Scientific Reports
|November 29, 2024
PubMed
Summary
This summary is machine-generated.

A new lightweight convolutional neural network (CNN) rapidly diagnoses COVID-19 from chest X-rays. This AI model achieves high accuracy, aiding medical professionals in quick and efficient disease detection.

Keywords:
COVID-19Chest x-ray imagingComputer-aided diagnosisConvolutional neural networksLightweight architecture

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

14.1K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.0K

Related Experiment Videos

Last Updated: Jun 6, 2025

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

1.7K
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

14.1K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.0K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Science

Background:

  • Accurate and rapid diagnosis of COVID-19 is crucial for effective patient management and disease control.
  • Chest X-ray imaging is a widely available and cost-effective tool for diagnosing respiratory illnesses.
  • Existing deep learning models for medical image analysis can be computationally intensive, limiting real-time applications.

Purpose of the Study:

  • To develop a lightweight and efficient convolutional neural network (CNN) architecture for the rapid detection of COVID-19 from chest X-ray images.
  • To improve the speed and reduce the computational requirements of AI models for COVID-19 diagnosis.
  • To validate the proposed CNN model using publicly available COVID-19 radiography datasets.

Main Methods:

  • Development of a novel CNN architecture featuring a redesigned feature extraction (FE) module and a multiscale feature (MF) module.
  • Training and validation of the CNN model on multiple versions of the COVID-19 Radiography Database.
  • Evaluation of model performance across three categories: COVID-19, viral/bacterial pneumonia, and normal chest X-ray images.

Main Results:

  • The proposed CNN achieved a training accuracy of 99.85% and a validation accuracy of 96.28% for the three-class classification task.
  • Optimal test set accuracies were 96.03% for COVID-19, 97.10% for viral/bacterial pneumonia, and 97.86% for normal cases.
  • The model demonstrated reduced computational requirements and improved processing speed compared to existing methods.

Conclusions:

  • The developed lightweight CNN offers a rapid and accurate solution for COVID-19 diagnosis using chest X-rays.
  • The proposed architecture effectively extracts relevant features for distinguishing between COVID-19, pneumonia, and normal cases.
  • This AI tool has the potential to support medical professionals in real-time clinical decision-making for COVID-19 detection.