A Review of ICBHI 2017 Respiratory Sounds Analysis using Deep Learning

Abstract

 Death rates across the globe are often linked to respiratory illnesses, with severe
conditions like- chronic obstructive pulmonary disease (COPD) and asthma being the primary culprits.
Early detection of the-se diseases in their initial stages is more crucial than we may realize. The ancient
diagnostic technique of lung auscultation, where a stethoscope is placed on the lungs, is renowned but also
has inherent limitations and susceptibility to data distortion due to environmental variables. This led to the
deve-lopment of modern solutions, born out of necessity, to address these challenges innovative methods
that harness the power of deep learning algorithms to capture respiratory sounds more accurately. The
International Conference on Biomedical and Health Informatics (ICBHI) dataset, containing lung sound
recordings, is available to the machine learning community for research and development. Leveraging
machine learning and deep learning techniques, with the latter being a subset of machine learning, such as
convolutional neural networks, has enabled more accurate diagnoses compared to traditional auscultation
methods. These advanced algorithms have achieved impre-ssive voice classification accuracy rates,
outperforming conventional approaches. The fusion of cutting-edge technology and medical expertise has
the potential to revolutionize respiratory disease detection and management. Scientific investigations and
research have demonstrated that when utilizing )ICBHI 2017( data set, its precision varies from 42% to
90%.
The goal of this article is to review articles related to the use of deep learning algorithms, which
are combined in some articles with other machine learning algorithms, and the way they deal with the
ICBHI 2017 dataset.