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

You might also read

Related Articles

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

Sort by
Same author

Density-equalized RANDT scan matching with integrated outlier removal and point density uniformity.

PloS one·2026
Same author

Volumetric comparison of gross tumor volume between PET-CT fused and CT based simulation in head and neck radiotherapy planning.

Journal of cancer research and therapeutics·2026
Same author

Hybrid GAN-LSTM framework for diabetic foot ulcer image synthesis and automated diagnosis.

Frontiers in medicine·2026
Same author

Swallow Winged Kite Optimization with Shuffle Attention Xtreme Gradient Boost Network for lung cancer detection using CT image.

European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society·2025
Same author

Enhancing U-Net for image denoising with bilateral filter noise residue and gradient estimation (BIRUNet).

Scientific reports·2025
Same author

A novel Convolutional Shuffle Attention Xtreme Gradient Boost Network for improved lung cancer detection using computed tomography images.

Computational biology and chemistry·2025

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 Based on Lung Ct Scan Using Deep Learning Techniques.

S V Kogilavani1, J Prabhu2, R Sandhiya1

  • 1. Department of Computer Science and Engineering, Kongu Engineering College, Perundurai, Erode 638060, Tamil Nadu, India.

Computational and Mathematical Methods in Medicine
|February 4, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces deep learning models for COVID-19 detection using CT scans, addressing RT-PCR kit shortages. The VGG16 model achieved the highest accuracy at 97.68% for distinguishing COVID-19 patients.

More Related Videos

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
07:53

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

1.6K
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

2.1K

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
Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
07:53

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

1.6K
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

2.1K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Infectious Diseases

Background:

  • The COVID-19 pandemic, caused by SARS-CoV-2, presents significant global health challenges.
  • Diagnostic limitations, including shortages of real-time reverse transcriptase-polymerase chain reaction (RT-PCR) kits, hinder timely patient management.
  • Radiological imaging, particularly computed tomography (CT) scans, offers a valuable alternative for disease detection.

Purpose of the Study:

  • To evaluate the efficacy of various deep learning algorithms for detecting COVID-19 from CT scans.
  • To identify the most accurate deep learning architecture for COVID-19 diagnosis using CT imaging.

Main Methods:

  • A dataset of 3873 CT scan images, labeled as "COVID" and "Non-COVID," was utilized.
  • Convolutional Neural Network (CNN) architectures including VGG16, DenseNet121, MobileNet, NASNet, Xception, and EfficientNet were employed.
  • The dataset was systematically divided into training, testing, and validation sets for model evaluation.

Main Results:

  • VGG16 achieved an accuracy of 97.68%.
  • DenseNet121 demonstrated 97.53% accuracy.
  • MobileNet, NASNet, Xception, and EfficientNet showed accuracies of 96.38%, 89.51%, 92.47%, and 80.19%, respectively.
  • VGG16 outperformed other evaluated architectures in COVID-19 detection accuracy.

Conclusions:

  • Deep learning models, particularly VGG16, show high potential for accurate COVID-19 detection using CT scans.
  • CT-based deep learning analysis can serve as a viable alternative or supplement to RT-PCR testing.
  • Further research can optimize these AI models for widespread clinical application in pandemic scenarios.