Deep Learning-Based Classification of Common Lung Sounds via Auto-Detected Respiratory Cycles

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dc.contributor.authorEngin, Mustafa Alptekin
dc.contributor.authorUzun Arslan, Rukiye
dc.contributor.authorSenyer Yapici, Irem
dc.contributor.authorAras, Selim
dc.contributor.authorGangal, Ali
dc.date.accessioned2026-09-01T15:52:34Z
dc.date.available2026-09-01T15:52:34Z
dc.date.issued2026
dc.departmentBayburt Üniversitesi
dc.description.abstractChronic respiratory diseases, the third leading cause of mortality on a global scale, can be diagnosed at an early stage through non-invasive auscultation. However, effective manual differentiation of lung sounds (LSs) requires not only sharp auditory skills but also significant clinical experience. With technological advancements, artificial intelligence (AI) has demonstrated the capability to distinguish LSs with accuracy comparable to or surpassing that of human experts. This study broadly compares the methods used in AI-based LSs classification. Firstly, respiratory cycles-consisting of inhalation and exhalation parts in LSs of different lengths depending on individual variability, obtained and labelled under expert guidance-were automatically detected using a series of signal processing procedures and a database was obtained in this way. This database of common LSs was then classified using various time-frequency representations such as spectrograms, scalograms, Mel-spectrograms and gammatonegrams for comparison. The utilisation of proven, convolutional neural network (CNN)-based pre-trained models through the application of transfer learning facilitated the comparison, thereby enabling the acquisition of the features to be employed in the classification process. The performances of CNN, CNN and Long Short-Term Memory (LSTM) hybrid architecture and support vector machine methods were compared in the classification process. When the spectral structure of gammatonegrams, which capture the spectral structure of signals in the low-frequency range with high fidelity and their noise-resistant structures, is combined with a CNN architecture, the best classification accuracy of 97.3% +/- 1.9 is obtained.
dc.description.sponsorshipScientific and Technological Research Council of Trkiye [116E003] -- The construction of the database used in this study was funded by the Scientific and Technological Research Council of Turkiye (grant number: 116E003).
dc.identifier.doi10.3390/bioengineering13020170
dc.identifier.issn2306-5354
dc.identifier.issue2
dc.identifier.orcid0000-0001-7276-3654
dc.identifier.pmid41749710
dc.identifier.scopus2-s2.0-105031516088
dc.identifier.scopusqualityN/A
dc.identifier.urihttp://dx.doi.org/10.3390/bioengineering13020170
dc.identifier.urihttps://hdl.handle.net/20.500.12403/8452
dc.identifier.volume13
dc.identifier.wosWOS:001700624900001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherMDPI
dc.relation.ispartofBioengineering-Basel
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260820
dc.subjectLung Sounds
dc.subjectRespiratory Cycle
dc.subjectAutomatic Recognition
dc.subjectDeep Learning
dc.subjectTime-Frequency Representations
dc.subjectClassification
dc.titleDeep Learning-Based Classification of Common Lung Sounds via Auto-Detected Respiratory Cycles
dc.typeArticle

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