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Related Experiment Video

Updated: Oct 28, 2025

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

114

Bayesian convolutional neural network estimation for pediatric pneumonia detection and diagnosis.

Vandecia Fernandes1, Geraldo Braz Junior1, Anselmo Cardoso de Paiva1

  • 1Federal University of Maranhão, Applied Computing Group - NCA, Av. dos Portugueses, 1996, Campus do Bacanga, São Luís, Maranhão 65080-805, Brazil.

Computer Methods and Programs in Biomedicine
|July 17, 2021
PubMed
Summary

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This study developed a convolutional neural network (CNN) using Bayesian optimization for accurate pneumonia detection and classification. The AI model achieved high accuracy, aiding in faster diagnosis for children.

Area of Science:

  • Medical imaging analysis
  • Artificial intelligence in healthcare
  • Pediatric respiratory diseases

Background:

  • Pneumonia is the leading cause of death in children under five globally.
  • Accurate and rapid diagnosis is crucial for reducing pneumonia mortality.
  • Computational methods can assist specialists and improve diagnostic accuracy.

Purpose of the Study:

  • To develop an automated method for pneumonia detection and classification.
  • To utilize convolutional neural networks (CNNs) for improved diagnostic speed and accuracy.
  • To explore Bayesian optimization for efficient CNN architecture design.

Main Methods:

  • A specific CNN architecture was constructed using Bayesian optimization.
  • The method leveraged pre-trained networks for pneumonia detection.
Keywords:
Bayesian optimizationConvolutional neural networksPneumonia detectionTransfer learning

Related Experiment Videos

Last Updated: Oct 28, 2025

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

114
  • Classification of viral and bacterial pneumonia types was performed.

    • Main Results:

    • The proposed CNN achieved 0.964 accuracy for pneumonia detection.
    • Pneumonia type classification accuracy reached 0.957.
    • The model demonstrated high performance without traditional image preprocessing.

    Conclusions:

    • Bayesian optimization is efficient for designing CNNs for pneumonia diagnosis.
    • The proposed CNN architecture is effective for detecting and classifying pneumonia.
    • The method's ability to perform without preprocessing highlights its efficiency and high performance.