Classification of Lung Sounds with Deep Learning

Abstract

Lung diseases are among the diseases that seriously threaten human health, and many deaths today are caused by lung diseases. Thanks to the lung sounds, important inferences can be made about lung diseases. Doctors often use the auscultation technique to evaluate patients with lung conditions. However, this technique has some drawbacks. For example, this may lead to a misdiagnosis if the doctor has not received a good medical education. In addition, since the lung sounds are nonstationary, the analysis and recognition process is complex. Therefore, the development of automatic recognition systems will help in making more precise and accurate diagnoses. Many studies based on traditional sound processing routines have been proposed to diagnose lung diseases and to assist professionals in their diagnosis. In this study, a method based on deep learning has been proposed for the classification of lung sounds. For this purpose, the Convolutional Neural Network (CNN) has been designed. In addition, experiments are carried out using different machine learning methods based on feature extraction. Experiments to evaluate the effectiveness of different methods are carried out using the ICBHI 2017 data set consisting of four classes commonly used in the literature. On average, 64.5% accuracy is obtained from the proposed method. In addition, when the results obtained from the proposed method are compared with the latest methods in the literature, it is seen that it has a better performance in terms of classification success.

Keywords

• lung sounds classification
• convolutional neural network
• deep learning