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 Experiment Video

Updated: Oct 4, 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

14.4K

COVID-19 detection from chest x-ray using MobileNet and residual separable convolution block.

V Santhosh Kumar Tangudu1, Jagadeesh Kakarla1, Isunuri Bala Venkateswarlu1

  • 1Indian Institute of Information Technology, Design and Manufacturing, Kancheepuram, Chennai, India.

Soft Computing
|February 2, 2022
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Three-class brain tumor classification using deep dense inception residual network.

Soft computing·2021
Same journal

Retraction Note: An automatic and intelligent brain tumor detection using Lee sigma filtered histogram segmentation model.

Soft computing·2026
Same journal

Retraction Note: A review on quantum computing and deep learning algorithms and their applications.

Soft computing·2026
Same journal

Retraction Note: Analyzing fibrous tissue pattern in fibrous dysplasia bone images using deep R-CNN networks for segmentation.

Soft computing·2026
Same journal

Retraction Note: Quantum K-means clustering method for detecting heart disease using quantum circuit approach.

Soft computing·2026
Same journal

Retraction Note: DenseNet-II: an improved deep convolutional neural network for melanoma cancer detection: Nancy Girdhar.

Soft computing·2026
Same journal

Retraction Note: Region of interest-based predictive algorithm for subretinal hemorrhage detection using faster R-CNN.

Soft computing·2026
See all related articles

This study introduces an efficient deep learning model for rapid COVID-19 detection using chest X-rays. The novel approach achieves 99% accuracy with reduced training time, aiding early diagnosis.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Infectious Diseases

Background:

  • The COVID-19 pandemic significantly impacts global health and economies.
  • Early detection of COVID-19 is crucial for patient survival.
  • Chest radiography offers a rapid and cost-effective diagnostic method.

Purpose of the Study:

  • To develop an automated COVID-19 detection system using chest X-ray images.
  • To address the high training time limitations of existing deep learning models.
  • To improve the accuracy and efficiency of COVID-19 diagnosis.

Main Methods:

  • Utilized transfer learning with a pre-trained MobileNet model.
  • Introduced a novel residual separable convolution block to enhance MobileNet performance.

More Related Videos

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

667

Related Experiment Videos

Last Updated: Oct 4, 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

14.4K
Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

667
  • Evaluated the model on two public datasets: COVID5K and COVIDRD.

    • Main Results:

    • Achieved 99% accuracy in COVID-19 detection on both datasets.
    • Demonstrated superior performance compared to existing state-of-the-art and pre-trained models.
    • Maintained high performance on noisy datasets.
    • Showcased reduced training time and fewer parameters compared to existing models.

    Conclusions:

    • The proposed model offers a highly accurate and efficient solution for automated COVID-19 detection from chest X-rays.
    • The model's efficiency in terms of training time and parameters makes it suitable for mobile applications.
    • This advancement can aid in faster and more accessible COVID-19 diagnosis globally.