Turkan, B.Ates, A.G.Ozdemir, O.Sonmez, E.B.2024-07-182024-07-1820229781665470100https://doi.org/10.1109/UBMK55850.2022.9919567https://hdl.handle.net/11411/64417th International Conference on Computer Science and Engineering, UBMK 2022 -- 14 September 2022 through 16 September 2022 -- -- 183844The aim of this research is to classify recorded chest sounds to distinguish among Asthma, Bronchiolitis, Bronchiectasis, COPD, Pneumonia and URTI diseases versus Healthy sound. That is, this paper introduces and challenges a seven- class problem using one of the few publicly available collection of sounds, the Respiratory Sound database from Kaggle. The performance of several deep learning algorithms has been compared and the Convolutional Neural Network architecture resulted in the most successful model. Unlike previous papers which worked on a subset of this database, this work proposes a more comprehensive seven-class challenge to distinguish among all diseases sampled in the database. The performance of several deep-learning algorithms has been compared and the best model is described in detail. © 2022 IEEE.eninfo:eu-repo/semantics/closedAccessDeep LearningDiagnosing From SoundNeural NetworksConvolutional Neural NetworksDatabase SystemsDeep LearningDiagnosisNetwork ArchitectureBreathing SoundsBronchiolitisConvolutional Neural NetworkDeep LearningDiagnosing From SoundNeural Network ArchitectureNeural-NetworksPerformanceRespiratory SoundsSound DatabaseLearning AlgorithmsConference Object2-s2.0-8514182618210.1109/UBMK55850.2022.9919567130N/A125