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Related Concept Videos

Pneumonia III: Complications and Assessment01:30

Pneumonia III: Complications and Assessment

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Pneumonia poses the potential for numerous complications that warrant consideration. These complications include the following:
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Pneumothorax-II01:27

Pneumothorax-II

457
Pneumothorax is a medical condition defined by the buildup of air in the pleural space between the lungs and the chest wall. This accumulation of air can lead to partial or complete lung collapse, resulting in a range of clinical manifestations. Understanding the clinical presentation and effective management strategies is crucial for healthcare professionals in providing timely and appropriate care to individuals with pneumothorax.
Clinical Manifestations:
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Related Experiment Video

Updated: Oct 14, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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Published on: December 19, 2020

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A Transfer Learning-Based Approach with Deep CNN for COVID-19- and Pneumonia-Affected Chest X-ray Image

Soarov Chakraborty1, Shourav Paul1, K M Azharul Hasan1

  • 1Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna, 9203 Bangladesh.

SN Computer Science
|November 1, 2021
PubMed
Summary
This summary is machine-generated.

This study accurately classifies COVID-19, Pneumonia, and Healthy cases from X-ray images using VGG-19 transfer learning. The model achieved high accuracy, aiding early disease identification.

Keywords:
COVID-19Deep learningMongoDBPneumoniaTransfer learning

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

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Area of Science:

  • Radiology
  • Computer Science
  • Medical Imaging Analysis

Background:

  • The COVID-19 pandemic necessitates rapid identification of affected patients.
  • Chest X-rays are crucial for diagnosing respiratory illnesses like COVID-19 and Pneumonia.
  • Accurate and early diagnosis is vital for effective patient management and public health.

Purpose of the Study:

  • To develop and evaluate a deep learning model for classifying COVID-19, Pneumonia, and Healthy cases using chest X-ray images.
  • To leverage transfer learning with a pre-trained VGG-19 architecture for improved classification performance.
  • To establish a reliable automated system for aiding in the diagnosis of these respiratory conditions.

Main Methods:

  • Utilized a dataset of 3797 chest X-ray images, categorized into COVID-19, Pneumonia, and Healthy.
  • Applied transfer learning with the pre-trained VGG-19 convolutional neural network architecture.
  • Employed MongoDB for efficient storage and retrieval of image data and associated labels.

Main Results:

  • Achieved a high classification accuracy of 97.11% on the test dataset.
  • Obtained an average precision of 97% for the classification task.
  • Demonstrated an average Recall of 97%, indicating effective identification of positive cases.

Conclusions:

  • The VGG-19 transfer learning approach is highly effective for classifying COVID-19, Pneumonia, and Healthy cases from chest X-rays.
  • The model's high accuracy, precision, and recall suggest its potential as a valuable tool in clinical settings.
  • Early and accurate detection of COVID-19 and Pneumonia through AI-powered image analysis can significantly improve patient outcomes.