ABSTRACT
Lung auscultation is one of the most common methods for screening of lung diseases. The increasingly high rate of respiratory diseases leads to the need for robust methods to detect the abnormalities in patients' breathing sounds. Lung sounds analysis stands out as a promising approach to automatic screening of lung diseases, serving as a second opinion for doctors as a stand-alone device for preliminary screening of lung diseases in remote areas. In previous research on lung classification using ICBHI Database on Kaggle, lung audios are converted to spectral images and fed into deep neural networks for training. There are a few studies which uses the scalogram, however they focussed on classification among different lung diseases. The use of scalograms in categorising the sound types are rarely used. In this paper, we combined scalograms and neural networks for classification of lung sound types. Padding methods and augmentation are also considered to evaluate the impacts on classification score. An ensemble learning is incorporated to increase classification accuracy by utilising voting of many models. The model trained and evaluated has shown prominent improvement of this method on classification on the benchmark ICBHI database.
