A Lightweight CNN Model for Detecting Respiratory Diseases from Lung Auscultation Sounds Using EMD-CWT-Based Hybrid Scalogram

Listening to lung sounds through auscultation is vital in examining the respiratory system for abnormalities. Automated analysis of lung auscultation sounds can be beneficial to the health systems in low-resource settings where there is a lack of skilled physicians. In this work, we propose a lightw...

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Bibliographic Details
Main Authors:	Ali, S.N (Author), Bhuiyan, M.I.H (Author), Hasan, T. (Author), Shuvo, S.B (Author), Swapnil, S.I (Author)
Format:	Article
Language:	English
Published:	Institute of Electrical and Electronics Engineers Inc. 2021
Subjects:	abnormal respiratory sound accuracy algorithm Article artificial intelligence auscultation Auscultation Automated analysis Biological organs breathing pattern bronchiectasis continuous wavelet transform Continuous wavelet transform Continuous wavelet transforms convolutional neural network Convolutional neural networks cumulative scale empirical mode decomposition Empirical Mode Decomposition entropy feature extraction Feature sets Fourier transform Health systems human Humans learning algorithm lightweight convolutional neural networks Low-resource settings lung Lung lung auscultation Lung auscultation sound Lung sounds machine learning Neural Networks, Computer Pulmonary diseases respiratory disease detection Respiratory Sounds Respiratory system respiratory tract disease scalogram sensitivity and specificity signal processing Signal processing spirometry Statistical tests training validation process Validation test wavelet analysis Wavelet Analysis Wavelet decomposition wheezing
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A Lightweight CNN Model for Detecting Respiratory Diseases from Lung Auscultation Sounds Using EMD-CWT-Based Hybrid Scalogram

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