A comprehensive review of computerized respiratory sound analysis and deep learning techniques for acoustic signal-based disease classification

What is it about?

Respiratory ailments stand out as one of the most prevalent health concerns globally, a designation underscored by the World Health Organization (WHO). Auscultation, the method of listening to sounds produced by the respiratory system, remains a cornerstone in diagnosing such conditions. However, recent progress in Computerized Respiratory Sound Analysis (CRSA) has revolutionized this diagnostic landscape, offering a quantitative approach to recording, manipulating, and contrasting respiratory sounds. This advancement in technology not only simplifies the process of auscultation but also creates opportunities for training artificial intelligence models to analyze auscultatory data in depth. In contrast to other review studies focusing on the classification and detection of lung diseases in 2023 and 2024, research endeavors employing deep learning algorithms were undertaken to examine signal analysis aimed at diagnosing lung diseases.

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Why is it important?

This study presents an extensive examination of the utilization of machine learning and deep learning techniques for the recognition of lung diseases through acoustic signal analysis. This study is expected to aid physicians and researchers who work on sound signal-based deep learning significantly.