Employing deep learning for lung sounds classification

Huda Dhari Satea, Amer Saleem Elameer, Ahmed Hussein Salman, Shahad Dhari Sateaa


Respiratory diseases indicate severe medical problems. They cause death for more than three million people annually according to the world health organization (WHO). Recently, with corona virus disease 19 (COVID-19) spreading the situation has become extremely serious. Thus, early detection of infected people is very vital in limiting the spread of respiratory diseases and COVID-19. In this paper, we have examined two different models using convolution neural networks. Firstly, we proposed and build a convolution neural network (CNN) model from scratch for classification the lung breath sounds. Secondly, we employed transfer learning using the pre-trained network AlexNet applying on the similar dataset. Our proposed model achieved an accuracy of 0.91 whereas the transfer learning model performing much better with an accuracy of 0.94.


Convolution neural network; Deep learning; Lung sounds; Respiratory diseases; Transfer learning;

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DOI: http://doi.org/10.11591/ijece.v12i4.pp4345-4351

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International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578

This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).