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  1. Home
  3. Lung Disease Recognition Methods Using Audio-based Analysis With Machine Learning.
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  3. Lung Disease Recognition Methods Using Audio-based Analysis With Machine Learning.

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Lung disease recognition methods using audio-based analysis with machine learning.

Ahmad H Sabry1, Omar I Dallal Bashi2, N H Nik Ali3

  • 1Department of Medical Instrumentation Engineering Techniques, Shatt Al-Arab University College, Basra, Iraq.

Heliyon
|February 29, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

Computer analysis of lung sounds improves respiratory disease diagnosis by reducing subjectivity. Machine learning shows promise for classifying lung conditions, but large-scale studies are needed for clinical adoption.

Keywords:
Audio processingAudio-based analysisClassificationFeature extractionLung disease recognitionLung soundsMachine learningRespiratory sounds

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Area of Science:

  • Medical Diagnostics
  • Computational Biology
  • Signal Processing

Background:

  • Automated computer-based approaches and advanced recording techniques enhance lung sound diagnostics, minimizing subjectivity.
  • Computer-based lung sound analysis enables thorough evaluation of lung sound features, including behavior analysis, measurement, noise suppression, and graphical representation.

Purpose of the Study:

  • To provide an overview of computer-based lung sound analysis for respiratory disease diagnosis.
  • To discuss the methodology, datasets, feature extraction, pre-processing, artifact removal, sound separation, and machine learning algorithms used in lung sound analysis.
  • To identify literature gaps and highlight the potential of machine learning in classifying respiratory diseases.

Main Methods:

  • Literature review and survey of existing studies on sound-based lung disease classification.
  • Discussion of machine learning algorithms, including deep learning and wavelet transform, applied to lung audio signals.
  • Analysis of datasets, feature extraction, pre-processing, and artifact removal techniques.

    • Main Results:

    • Machine learning algorithms applied to lung sound analysis demonstrate promising results for respiratory disease classification.
    • The study identifies key elements and methodologies in sound-based lung disease diagnosis.
    • Literature gaps in large-scale investigations for clinical adoption are highlighted.

    Conclusions:

    • Sound-based machine learning offers a promising avenue for the classification of respiratory diseases.
    • Further large-scale investigations are crucial to validate findings and promote widespread clinical adoption.
    • The findings are valuable for physicians and researchers in the field of sound-signal-based machine learning.