Analysis of unlabeled lung sound samples using semi-supervised convolutional neural networks

Lung sounds convey valuable information relevant to human respiratory health. Therefore, it is important to classify lung sounds for early diagnoses of respiratory disorders. In recent years, computerized lung sound analysis with machine learning algorithms has attracted researchers, especially the state-of-the-art convolutional neural network (CNN). However, most of these algorithms require a large number of labeled respiratory sound samples, which is time- and cost-consuming. Based on a four-layers CNN, this study proposes graph semi-supervised CNNs (GS-CNNs), which can classify respiratory sounds into normal, crackle and wheeze ones with only a small labeled sample size and a large unlabeled sample size. The graph of respiratory sounds (Graph-RS) with labeled and unlabeled respiratory sound samples as vertexes is first constructed, which can indicate not only the reasonable metric information but also the relationship of all the samples. Then, GS-CNNs are developed by adding the information extracted from Graph-RS to the loss function of the original CNN. The added information enables the GS-CNNs to regulate the structure of the original CNN, thus enhancing classification accuracy. The GS-CNNs are evaluated by experiments with the samples collected by electronic stethoscope. Results demonstrate that the proposed GS-CNNs outperform the original CNN, and that the more information from Graph-RS is used, the better recognition effect will be achieved. (C) 2021 Published by Elsevier Inc.

Automated summary

This study proposes a new method for lung sound classification, which can classify respiratory sounds into different categories with a small number of labeled samples and a large number of unlabeled samples, using graph semi-supervised CNN technology, outperforming traditional CNN methods.