Lung Sound Classification for Respiratory Disease Identification Using Deep Learning: A Survey

Thinira Wanasinghe; Sakuni Bandara; Supun Madusanka; Dulani Meedeniya; Meelan Bandara; Isabel de la Torre Díez

doi:10.3991/ijoe.v20i10.49585

Authors

Thinira Wanasinghe Department of Computer Science and Engineering, University of Moratuwa, Moratuwa, 10400,Sri Lanka https://orcid.org/0009-0006-1719-7585
Sakuni Bandara Department of Computer Science and Engineering, University of Moratuwa, Moratuwa, 10400,Sri Lanka https://orcid.org/0009-0003-1840-2497
Supun Madusanka Department of Computer Science and Engineering, University of Moratuwa, Moratuwa, 10400,Sri Lanka https://orcid.org/0009-0007-1030-4832
Dulani Meedeniya University of Moratuwa https://orcid.org/0000-0002-4520-3819
Meelan Bandara Department of Computer Science and Engineering, University of Moratuwa, Moratuwa, 10400,Sri Lanka
Isabel de la Torre Díez Department of Signal Theory and Communications, and Telematics Engineering, University of Valladolid, 15, Paseo de Belén, 47011 Valladolid, Spain.

DOI:

https://doi.org/10.3991/ijoe.v20i10.49585

Keywords:

artificial intelligence, Classification, Explainability, Respiratory diseases, Sound processing

Abstract

Integrating artificial intelligence (AI) into lung sound classification has markedly improved respiratory disease diagnosis by analysing intricate patterns within audio data. This study is driven by the widespread issue of lung diseases, which affect around 500 million people globally. Early detection of respiratory diseases is crucial for delivering timely and effective treatment. Our study consists of a comprehensive survey of lung sound classification methodologies, exploring the advancements made in leveraging AI to identify and classify respiratory diseases. This survey thoroughly investigates lung sound classification models, along with data augmentation, feature extraction, explainable techniques and support tools to improve systems for diagnosing respiratory conditions. Our goal is to provide meaningful insights for healthcare professionals, researchers and technologists who are dedicated to developing methodologies for the early detection of pulmonary diseases. The paper provides a summary of the current status of lung sound classification research, highlighting both advancements and challenges in the use of AI for more accurate and efficient diagnostic methods in respiratory healthcare.

Author Biography

Dulani Meedeniya, University of Moratuwa

Prof. Dulani Meedeniya is a Professor in Computer Science and Engineering at the University of Moratuwa, Sri Lanka. She holds a PhD in Computer Science from the University of St Andrews, United Kingdom. She is the director of the Bio-Health Informatics group at her department and engages in many collaborative research. She is a co-author of 100+ publications in indexed journals, peer-reviewed conferences and book chapters. Prof. Dulani has received several awards and grants for her contribution in research. She serves as a reviewer, program committee and editorial team member in many international conferences and journals. Her main research interests are Software modelling and design, Bio-Health Informatics, Deep Learning and Technology-enhanced learning. She is a Fellow of HEA (UK), MIET, MIEEE, Member of ACM and a Chartered Engineer registered at EC (UK).