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: Sep 22, 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

14.3K

Deep GRU-CNN Model for COVID-19 Detection From Chest X-Rays Data.

Pir Masoom Shah1,2, Faizan Ullah1, Dilawar Shah1

  • 1Department of Computer ScienceBacha Khan University Charsadda 24000 Pakistan.

IEEE Access : Practical Innovations, Open Solutions
|May 18, 2022
PubMed
Summary

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

Morpho-anatomical, physiological and biochemical responses of <i>Neltuma juliflora</i> to heavy metals under vehicular pollution.

International journal of phytoremediation·2026
Same author

Improving cognitive stress classification via multimodal EEG and ECG fusion: gender differences in physiological response.

Scientific reports·2026
Same author

AI-driven intrusion detection and lightweight authentication framework for secure and efficient medical sensor networks.

Scientific reports·2025
Same author

DSSCC net enhanced skin cancer classification using SMOTE Tomek and optimized convolutional neural network.

Scientific reports·2025
Same author

Biogenic synthesis of silver nanoparticles using Zaleya pentandra and investigation of their biological activities.

Scientific reports·2025
Same author

Enhancing brain tumor segmentation using attention based convolutional UNet on MRI images.

Scientific reports·2025

This study introduces a hybrid deep learning model using convolutional neural networks (CNN) and gated recurrent units (GRU) for rapid COVID-19 detection from chest X-rays (CXRs). The model shows high accuracy, aiding early diagnosis and disease mitigation.

Area of Science:

  • Medical imaging analysis
  • Artificial intelligence in healthcare
  • Deep learning for disease detection

Background:

  • Exponential data growth necessitates advanced analytical techniques in medicine.
  • Automated disease detection, particularly for COVID-19, is crucial for reducing mortality rates.
  • Resource limitations in managing widespread outbreaks like COVID-19 highlight the need for rapid diagnostic tools.

Purpose of the Study:

  • To propose a hybrid deep learning model for automated COVID-19 detection using chest X-rays (CXRs).
  • To evaluate the model's performance in distinguishing between COVID-19, Pneumonia, and Normal cases.
  • To demonstrate the potential of AI in accelerating viral disease diagnosis.

Main Methods:

  • Development of a hybrid deep learning model combining Convolutional Neural Network (CNN) for feature extraction and Gated Recurrent Unit (GRU) for classification.
Keywords:
CNNCOVID-19GRUMedical datachest X-raysdeep learning

More Related Videos

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

2.0K
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: Sep 22, 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

14.3K
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

2.0K
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
  • Training the model on a dataset of 424 CXR images across three classes: COVID-19, Pneumonia, and Normal.
  • Utilizing precision, recall, and F1-score metrics to assess model performance.

    • Main Results:

    • The proposed CNN-GRU model achieved high performance metrics: 0.96 precision, 0.96 recall, and 0.95 F1-score.
    • The model demonstrated significant capability in accurately classifying COVID-19, Pneumonia, and Normal CXRs.
    • The results underscore the effectiveness of deep learning in analyzing medical images for disease detection.

    Conclusions:

    • Deep learning models, like the proposed hybrid CNN-GRU, can significantly contribute to the early and accurate detection of COVID-19 from CXRs.
    • This approach offers a valuable tool for medical practitioners, potentially mitigating the impact of the pandemic.
    • The study highlights the role of AI in enhancing diagnostic capabilities within healthcare systems.