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

509
Pneumonia poses the potential for numerous complications that warrant consideration. These complications include the following:
509

You might also read

Related Articles

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

Sort by
Same author

A Hybrid Search Behavior-Based Adaptive Grey Wolf Optimizer for Cooperative Path Planning for Multiple UAVs.

Sensors (Basel, Switzerland)·2025
Same author

Improved deep neural network (EnhanceNet) for real-time detection of some publicly prohibited items.

Network (Bristol, England)·2024
Same author

Comparative performance analysis of Boruta, SHAP, and Borutashap for disease diagnosis: A study with multiple machine learning algorithms.

Network (Bristol, England)·2024
Same author

Lightweight Separable Convolution Network for Breast Cancer Histopathological Identification.

Diagnostics (Basel, Switzerland)·2023
Same author

EVAE-Net: An Ensemble Variational Autoencoder Deep Learning Network for COVID-19 Classification Based on Chest X-ray Images.

Diagnostics (Basel, Switzerland)·2022
Same author

Parallelistic Convolution Neural Network Approach for Brain Tumor Diagnosis.

Diagnostics (Basel, Switzerland)·2022

Related Experiment Video

Updated: Oct 2, 2025

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

Multi-Channel Based Image Processing Scheme for Pneumonia Identification.

Grace Ugochi Nneji1, Jingye Cai1, Jianhua Deng1

  • 1School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.

Diagnostics (Basel, Switzerland)
|February 25, 2022
PubMed
Summary

This study introduces a novel computer-aided diagnosis (CAD) system for identifying pneumonia from chest X-rays. The multi-channel deep learning approach achieves high accuracy, aiding in early detection of this severe respiratory infection.

Keywords:
COVID-19contrast enhanced canny edge detection (CECED)contrast limited adaptive histogram equalization (CLAHE)deep learningimage identificationlocal binary pattern (LBP)pneumonia disease

More Related Videos

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
Efficient Method for Imaging Murine Lungs that Preserves Spatial Dynamics of Fungal Spores in the Airways
10:06

Efficient Method for Imaging Murine Lungs that Preserves Spatial Dynamics of Fungal Spores in the Airways

Published on: December 13, 2024

512

Related Experiment Videos

Last Updated: Oct 2, 2025

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
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
Efficient Method for Imaging Murine Lungs that Preserves Spatial Dynamics of Fungal Spores in the Airways
10:06

Efficient Method for Imaging Murine Lungs that Preserves Spatial Dynamics of Fungal Spores in the Airways

Published on: December 13, 2024

512

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Pulmonology

Background:

  • Pneumonia is a significant global health concern, causing high mortality rates, particularly in young children and the elderly.
  • Accurate and timely diagnosis of pneumonia is crucial for effective treatment and public health management.
  • Existing diagnostic methods can be limited by factors such as image quality and the need for expert interpretation.

Purpose of the Study:

  • To develop and evaluate a multi-channel image processing scheme for automated pneumonia detection from chest X-ray (CXR) images.
  • To address challenges related to low image quality and improve the accuracy of pneumonia identification in CXR.
  • To present a deep learning model that integrates features from different image processing techniques for enhanced pneumonia classification.

Main Methods:

  • A novel multi-channel approach was employed, utilizing Local Binary Pattern (LBP), Contrast Enhanced Canny Edge Detection (CECED), and Contrast Limited Adaptive Histogram Equalization (CLAHE) processed CXR images.
  • Deep neural networks, including shallow CNN, pre-trained Inception-V3, and pre-trained MobileNet-V3, were used to extract features from the three distinct image channels.
  • Features from each channel were concatenated, and a softmax classifier was used for the final pneumonia identification.

Main Results:

  • The proposed deep learning network demonstrated high accuracy in classifying pneumonia from CXR images.
  • Experimental results on a public dataset reported an accuracy of 98.3%, sensitivity of 98.9%, and specificity of 99.2%.
  • The multi-channel approach achieved comparable performance to single-model and state-of-the-art methods.

Conclusions:

  • The developed multi-channel computer-aided diagnosis (CAD) system effectively identifies pneumonia from chest X-rays.
  • The proposed method shows significant potential for improving the accuracy and efficiency of pneumonia diagnosis.
  • This approach offers a promising tool for assisting clinicians in the early detection and management of pneumonia.