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Acoustic-Based Deep Learning Architectures for Lung Disease Diagnosis: A Comprehensive Overview.

Alyaa Hamel Sfayyih¹, Ahmad H Sabry², Shymaa Mohammed Jameel³

¹Department of Electrical and Electronic Engineering, Faculty of Engineering, University Putra Malaysia, Serdang 43400, Malaysia.

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|May 27, 2023

Summary

This review details deep learning for analyzing lung sounds, a key method for diagnosing respiratory conditions. It covers trends, datasets, and methods for improved computer-based respiratory sound analysis.

Keywords:

CNN acoustic signal analysis deep learning lung sound signals respiratory system signal analysis

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Area of Science:

Respiratory Medicine
Artificial Intelligence
Biomedical Signal Processing

Background:

Lung auscultation is a vital diagnostic tool for respiratory health, gaining attention post-pandemic.
Computer-based respiratory sound analysis, particularly using AI, offers advanced methods for detecting lung abnormalities.
Existing reviews lack specific focus on deep learning architectures for lung sound analysis.

Purpose of the Study:

To provide a comprehensive review of deep learning-based architectures for lung sound analysis.
To consolidate information on trends, datasets, features, and methods in this specialized field.
To identify gaps and suggest future research directions in AI-driven respiratory diagnostics.

Main Methods:

Systematic literature search across major scientific databases (PubMed, IEEE, Springer, etc.).
Extraction and assessment of over 160 publications on deep learning and lung sound analysis.
Analysis of common features, datasets, classification methods, and signal processing techniques.

Main Results:

Identified key trends in lung sound pathology and classification.
Summarized common features and datasets used in deep learning models for respiratory sounds.
Highlighted various signal processing techniques and classification methods employed.

Conclusions:

Deep learning offers significant potential for advancing computer-based respiratory sound analysis.
Further research is needed to refine models, improve data standardization, and explore novel architectures.
This review provides a foundation for future development in AI-assisted lung sound diagnostics.