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Related Experiment Video

Updated: Apr 8, 2026

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
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Novel deep learning-based optimization framework for the classification of respiratory diseases using lung sound

G Ayappan1, S Sumathi2, V Mani3

  • 1Department of Electronics and Communication Engineering, Sri Venkateswara College of Engineering, Sriperumbudur, 602117, India. ayappan01@gmail.com.

Scientific Reports
|April 6, 2026
PubMed
Summary

This study introduces an optimized deep learning framework for respiratory disease classification using lung sound analysis. The Enhanced Bidirectional Long Short-Term Memory with Average and Subtraction-Based Optimizer model significantly improves diagnostic accuracy.

Keywords:
Average and subtraction-based optimizerBand pass filteringDe-noising auto encoderEnhanced bi-directional long short-term memoryLung sound analysisRespiratory diseases classification

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence
  • Biomedical Signal Processing

Background:

  • Traditional respiratory disease diagnosis relies on imaging, which can be costly and invasive.
  • Lung sound analysis presents a non-invasive, cost-effective alternative for respiratory disease detection.
  • Automated classification of respiratory conditions using lung sounds requires robust feature extraction and classification models.

Purpose of the Study:

  • To propose and evaluate a deep learning-based optimization framework for automated respiratory disease classification using lung sound analysis.
  • To enhance the accuracy and reliability of respiratory disease classification through advanced signal processing and machine learning techniques.
  • To leverage the ICBHI 2017 Respiratory Sound Database for developing and validating the proposed classification model.

Main Methods:

  • Audio signals were preprocessed using band-pass filtering and a Denoising Autoencoder (DAE) for feature extraction.
  • An Enhanced Bidirectional Long Short-Term Memory (EBiLSTM) network with residual connections and regularization was used for classification.
  • Hyperparameters were optimized using the Average and Subtraction-Based Optimizer (ASBO) to maximize classification accuracy.

Main Results:

  • The proposed EBiLSTM-ASBO model demonstrated statistically significant improvements over baseline methods.
  • Overall accuracy improved by up to 18.51%, with consistent gains in precision, recall, and Matthews Correlation Coefficient (MCC).
  • Statistical hypothesis testing confirmed the robustness of the performance enhancements (p < 0.05).

Conclusions:

  • The developed deep learning framework effectively captures disease-specific acoustic patterns in lung sounds.
  • The proposed model offers a reliable approach for multi-class respiratory disease classification.
  • This non-invasive method shows promise for improving the diagnosis of respiratory conditions.