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

688
Pneumonia poses the potential for numerous complications that warrant consideration. These complications include the following:
688
Radiological Investigation III: Pulmonary Angiogram and PET Scan01:13

Radiological Investigation III: Pulmonary Angiogram and PET Scan

320
Radiological investigations are paramount in the diagnosis and management of various pulmonary diseases. Two essential investigations are the Pulmonary Angiogram and the Positron Emission Tomography (PET) Scan.
Pulmonary Angiogram
A Pulmonary Angiogram is an invasive procedure involving injecting a contrast medium through a catheter threaded into the pulmonary artery or the right side of the heart to visualize the pulmonary vasculature. Computed Tomography (CT) scans have mainly replaced this...
320
Pneumonia I: Introduction01:30

Pneumonia I: Introduction

620
Pneumonia is an acute respiratory infection that targets the lungs, specifically the alveoli. These tiny air sacs, essential for oxygen exchange, become engorged with pus and fluid, severely hindering breathing, decreasing oxygen absorption, and causing significant pain and discomfort during respiration.
Risk Factors
Various factors influence the likelihood of developing pneumonia. Age plays a crucial role, with infants, children under two, and individuals over 65 at increased risk due to their...
620
Pneumonia II: Pathophysiology01:29

Pneumonia II: Pathophysiology

2.3K
The pathophysiology of pneumonia involves the following steps:
2.3K

You might also read

Related Articles

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

Sort by
Same author

Gallstone disease classification using SLOA-optimized CatBoost classifier with explainable AI.

PloS one·2026
Same author

<i>Wearanize+</i>: a multimodal dataset for evaluating wearable technologies in sleep research.

Sleep advances : a journal of the Sleep Research Society·2026
Same author

Microclimate-Controlled Smart Growth Cabinets for High-Throughput Plant Phenotyping.

Sensors (Basel, Switzerland)·2025
Same author

Mean shift based prototypical network for steel surface anomaly recognition.

Scientific reports·2025
Same author

Breast Cancer Prediction Using Rotation Forest Algorithm Along with Finding the Influential Causes.

Bioengineering (Basel, Switzerland)·2025
Same author

Metaheuristic-Driven Feature Selection for Human Activity Recognition on KU-HAR Dataset Using XGBoost Classifier.

Sensors (Basel, Switzerland)·2025

Related Experiment Video

Updated: Dec 17, 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

14.6K

A Novel Method to Identify Pneumonia through Analyzing Chest Radiographs Employing a Multichannel Convolutional

Abdullah-Al Nahid1, Niloy Sikder2, Anupam Kumar Bairagi2

  • 1Electronics and Communication Engineering Discipline, Khulna University, Khulna 9208, Bangladesh.

Sensors (Basel, Switzerland)
|June 25, 2020
PubMed
Summary

This study introduces a novel machine learning approach for early pneumonia detection using chest X-rays. The developed model shows high potential for automated pneumonia diagnosis, improving patient outcomes.

Keywords:
chest radiographdeep learningmedical image processingpneumonia

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

1.9K
Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

339

Related Experiment Videos

Last Updated: Dec 17, 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

14.6K
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

1.9K
Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

339

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Pathology

Background:

  • Pneumonia is a leading cause of death, particularly in children, with millions affected annually.
  • Current diagnostic methods rely on expert interpretation of chest X-rays, facing limitations due to a shortage of trained professionals.
  • Early and accurate diagnosis is crucial for effective pneumonia treatment and improving survival rates.

Purpose of the Study:

  • To develop an automated system for detecting pneumonia using machine learning techniques.
  • To leverage deep learning algorithms for analyzing chest X-ray images for pneumonia diagnosis.
  • To address the diagnostic challenges posed by the high incidence of pneumonia globally.

Main Methods:

  • Utilized image processing and deep learning techniques for pneumonia detection.
  • Developed a novel diagnostic method based on analyzing chest X-ray images.
  • Tested the proposed method on a widely recognized chest radiography dataset.

Main Results:

  • The developed model demonstrated significant potential for automated pneumonia diagnosis.
  • The proposed method achieved accurate detection of pneumonia from chest X-ray images.
  • Results indicate the model's efficacy in a clinical setting.

Conclusions:

  • The novel image processing and deep learning method offers a promising solution for automated pneumonia detection.
  • This approach can help overcome the limitations of manual diagnosis, especially in resource-limited settings.
  • The developed model is a potent tool for integration into automatic pneumonia diagnosis schemes, improving healthcare accessibility and efficiency.