Jove
Visualize
Contact Us

Related Concept Videos

Computed Tomography01:10

Computed Tomography

7.3K
Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
7.3K
Imaging Studies for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

105
Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...
105

You might also read

Related Articles

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

Sort by
Same author

Integrating transfer learning with scalogram analysis for blood pressure estimation from PPG signals.

Scientific reports·2025
Same author

Semantic Concept Mining Based on Hierarchical Event Detection for Soccer Video Indexing.

Journal of multimedia·2022
Same author

Iterative Filtering Decomposition Based Early Dementia Diagnosis Using EEG With Cognitive Tests.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2020
Same author

Hidden Markov model based epileptic seizure detection using tunable Q wavelet transform.

Journal of biomedical research·2020
Same author

Multi-channel EEG based automatic epileptic seizure detection using iterative filtering decomposition and Hidden Markov Model.

Computers in biology and medicine·2020
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 30, 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

Deep learning empowered COVID-19 diagnosis using chest CT scan images for collaborative edge-cloud computing

Vipul Kumar Singh1, Maheshkumar H Kolekar1

  • 1Department of Electrical Engineering, Indian Institute of Technology, Patna, India.

Multimedia Tools and Applications
|July 5, 2021
PubMed
Summary
This summary is machine-generated.

A new deep learning model for COVID-19 diagnosis offers high accuracy (96.40%) and faster results. This optimized model is suitable for mobile devices, improving remote diagnostic capabilities.

Keywords:
COVID-19Chest CT scanDeep LearningDiagnosisEdge ComputingMobileNet V2

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.1K
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 30, 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
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.1K
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
  • Epidemiology

Background:

  • The COVID-19 pandemic necessitates rapid and accessible diagnostic methods.
  • Current clinical testing for COVID-19 is time-consuming and not widely accessible.
  • Deep learning models show promise for COVID-19 diagnosis but are often too complex for resource-constrained environments.

Purpose of the Study:

  • To develop a computationally efficient deep learning model for COVID-19 diagnosis.
  • To optimize a deep learning model for compatibility with mobile and edge devices.
  • To evaluate the diagnostic performance of the proposed model using chest CT scans.

Main Methods:

  • A fine-tuned deep learning model inspired by MobileNet V2 architecture was developed.
  • The model was optimized for size and complexity for mobile and edge deployment.
  • Extensive experiments were conducted on a dataset of 2482 chest CT scan images.

Main Results:

  • The developed model achieved a high classification accuracy of 96.40%.
  • The model demonstrated a response time approximately ten times shorter than prevailing deep learning models.
  • McNemar's statistical test confirmed the efficacy of the proposed model.

Conclusions:

  • The fine-tuned deep learning model provides an accurate and rapid solution for COVID-19 diagnosis.
  • The model's optimization makes it suitable for remote diagnostic systems on mobile and edge devices.
  • This approach offers a viable alternative to traditional clinical testing, especially in resource-limited settings.