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Related Concept Videos

Pneumonia I: Introduction01:30

Pneumonia I: Introduction

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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...
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Pneumonia II: Pathophysiology01:29

Pneumonia II: Pathophysiology

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The pathophysiology of pneumonia involves the following steps:
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Pneumonia IV: Management01:28

Pneumonia IV: Management

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The treatment of pneumonia varies based on its severity and the causative pathogen. Here is a structured approach to managing pneumonia, integrating pharmaceutical and supportive care strategies.
Bacterial Pneumonia Treatment
For bacterial pneumonia, antibiotics serve as the cornerstone of therapy. Initial treatment often begins with empirical antibiotics, tailored to the anticipated causative organism and adjusted based on culture results. Key antibiotic choices include:
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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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Pneumonia V: Nursing management and Prevention01:30

Pneumonia V: Nursing management and Prevention

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Nursing management of pneumonia involves promoting airway patency, facilitating rest and conserving energy, encouraging fluid intake, maintaining nutrition, and educating patients.
The nurse must practice strict medical asepsis and adhere to infection control guidelines to minimize healthcare-associated infections.
Enhance airway patency
Position the patient correctly to facilitate drainage of the affected lung segments. Manual or mechanical percussion and vibration can also be employed....
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Pulmonary Tuberculosis II01:28

Pulmonary Tuberculosis II

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Tuberculosis, or TB, is a bacterial infectious disease caused by Mycobacterium tuberculosis. While its primary impact is on the lungs, leading to pulmonary tuberculosis, it can also affect various other organs, a condition referred to as extrapulmonary tuberculosis.
Here is a detailed explanation of its pathophysiology:
Transmission: The process begins when a person inhales droplet nuclei containing M. tuberculosis. These are typically released into the air when an individual with pulmonary or...
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Related Experiment Video

Updated: Jan 11, 2026

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

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TL-PneuNet: a transfer learning-based pneumonia classification framework.

Biswajit Tripathy1, Shakir Khan2, Sujit Bebortta1

  • 1Department of Computer Science, Ravenshaw University, Cuttack, 753003, India.

Scientific Reports
|November 17, 2025
PubMed
Summary
This summary is machine-generated.

Transfer Learning (TL) improves pneumonia detection from chest X-rays. The ResNet152V2 model achieved 83.17% accuracy, aiding rapid diagnosis for healthcare professionals.

Keywords:
Chest X-ray datasetPneumonia predictionResNet152VTransfer learningVGG16Xception

Related Experiment Videos

Last Updated: Jan 11, 2026

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

407

Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Respiratory Medicine

Background:

  • Pneumonia is a serious lung infection causing fluid buildup in alveoli, posing a life-threatening risk if diagnosis is delayed.
  • Current diagnostic methods using limited radiation levels for chest X-rays can lead to unreliable pneumonia detection.
  • Transfer Learning (TL) presents a promising approach to enhance the accuracy and efficiency of pneumonia diagnosis.

Purpose of the Study:

  • To develop and evaluate a Transfer Learning (TL) model for accurate pneumonia prediction using chest X-ray images.
  • To compare the performance of different vision models (Xception, VGG16, ResNet152V2) within a TL framework for pneumonia classification.
  • To assess the potential of TL in assisting pulmonologists and physicians with rapid and precise pneumonia diagnoses.

Main Methods:

  • Utilized a dataset of 5856 highly imbalanced chest X-ray images for model training and evaluation.
  • Applied Transfer Learning (TL) techniques to adapt pre-trained vision models, including Xception, VGG16, and ResNet152V2.
  • Trained and fine-tuned selected deep learning models on the chest X-ray dataset to differentiate between normal and pneumonia cases.

Main Results:

  • The TL models demonstrated strong performance on the chest X-ray dataset, with accuracies of 80.45% (Xception), 80.77% (VGG16), and 83.17% (ResNet152V2).
  • ResNet152V2 exhibited superior performance compared to Xception and VGG16, achieving the highest accuracy.
  • The ResNet152V2 model attained a precision score of 79.87% and a recall score of 97.69% for pneumonia classification.

Conclusions:

  • The proposed TL framework effectively classifies pneumonia from chest X-ray images, highlighting the potential of deep learning in medical diagnostics.
  • ResNet152V2, when utilized with TL, demonstrates significant efficacy in identifying pneumonia, offering a reliable tool for clinical decision support.
  • This approach can empower healthcare professionals to achieve faster and more accurate diagnoses, potentially improving patient outcomes in pneumonia cases.