Anomaly Detection of Lung Sounds Using Deep Neural Networks

Abstract

To find lung diseases, physicians need to conduct various examinations. Recently, to reduce their burdens, many applications of deep learning have been proposed to diagnose chest X-ray images. However, there are few studies using deep learning for auscultation, and also, there are only a few small-scale benchmark datasets of lung sounds that are annotated for machine learning. Therefore, we aim to build an anomaly detection system that only uses normal data for the training. When building anomaly detection systems, it is important to capture generalized features based only on the normal data. To solve this problem, first, we propose some algorithms that improve the Deep Autoencoding Gaussian Mixture Model (DAGMM). Second, we propose some algorithms that improves Efficient GAN. Various types of neural networks such as CNN, LSTM, and convolutional LSTM (C-LSTM) are applied to DAGMM, and GMM and C-LSTM are applied to Efficient GAN for effective feature extraction. The experimental results show that each of the proposed methods has effective classification performance for lung sounds, and especially, the combination of convolution and LSTM, and the combination of feature extraction and GMM are effective for any of the models.