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

Pneumonia III: Complications and Assessment01:30

Pneumonia III: Complications and Assessment

523
Pneumonia poses the potential for numerous complications that warrant consideration. These complications include the following:
523

You might also read

Related Articles

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

Sort by
Same author

Unveiling the antifungal arsenal: proteomic profiling of tomato exosomes.

Planta·2026
Same author

SIFA: A two-stage adaptive ensemble framework for solar irradiance forecasting using a wrapper-based feature selection and chaotic manta ray optimization.

Scientific reports·2026
Same author

RNN-based detection of IoT malware using diverse feature engineering methods.

Scientific reports·2026
Same author

Malware detection in IoT networks with CNNs and integrated feature engineering.

Scientific reports·2026
Same author

Machine learning models for smart grid stability prediction: a comparative analysis.

Scientific reports·2026
Same author

Utilizing deep learning models for early detection and classification of fruit diseases: towards sustainable agriculture and enhanced food quality.

Scientific reports·2026

Related Experiment Video

Updated: Oct 14, 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

Two-Stage Deep Learning Framework for Discrimination between COVID-19 and Community-Acquired Pneumonia from Chest CT

Mohamed Abdel-Basset1, Hossam Hawash1, Nour Moustafa2

  • 1Faculty of Computers and Informatics, Zagazig University, Zagazig, Sharqiyah, 44519, Egypt.

Pattern Recognition Letters
|November 3, 2021
PubMed
Summary

This study presents a novel deep learning framework to accurately detect and differentiate COVID-19 from other pneumonias using lung CT scans. The approach enhances diagnostic capabilities for infectious lung diseases.

More Related Videos

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.9K
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 14, 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
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.9K
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
  • Pulmonology

Background:

  • COVID-19 poses a significant global health threat, necessitating advanced diagnostic tools.
  • Computed tomography (CT) is crucial for diagnosing COVID-19, but automated analysis is needed.
  • Distinguishing COVID-19 from community-acquired pneumonia (CAP) on CT scans remains challenging.

Purpose of the Study:

  • To develop an efficient deep learning (DL) framework for automated localization and discrimination of COVID-19 from CAP on lung CT scans.
  • To improve the accuracy and interpretability of AI-driven diagnostic systems for respiratory infections.

Main Methods:

  • A two-stage DL framework was designed, starting with a U-shaped network for lung infection segmentation.
  • Transfer learning and an attention mechanism were employed for robust feature extraction and classification.
  • An infection prediction module was introduced to guide classification decisions based on detected infection regions.

Main Results:

  • The proposed DL framework achieved high performance in segmenting lung infection areas.
  • The model demonstrated excellent classification accuracy, outperforming existing state-of-the-art methods.
  • The infection prediction module provided interpretable classification decisions.

Conclusions:

  • The novel two-stage DL framework offers an effective solution for automated COVID-19 diagnosis from CT scans.
  • This approach has the potential to aid radiologists in faster and more accurate disease identification.
  • The study highlights the utility of deep learning in medical image analysis for infectious diseases.