Employing deep learning for lung sounds classification

Abstract

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.

Portable spirometer using pressure-volume method with Bluetooth integration to Android smartphone

“…However, it can be extended to allow other values for further lung disease detection, as in [48]. Furthermore, machine learning algorithms can be used to enhance the accuracy of spirometer readings and COPD diagnosis classification [49]- [54]. NXP MPX5100…”