Efficiently Classifying Lung Sounds through Depthwise Separable CNN Models with Fused STFT and MFCC Features.

Shing-Yun Jung,Yu-Sheng Wu,Chuen-Tsai Sun,Shyan-Ming Yuan,Chia-Hung Liao

doi:10.3390/diagnostics11040732

Abstract

Lung sounds remain vital in clinical diagnosis as they reveal associations with pulmonary pathologies. With COVID-19 spreading across the world, it has become more pressing for medical professionals to better leverage artificial intelligence for faster and more accurate lung auscultation. This research aims to propose a feature engineering process that extracts the dedicated features for the depthwise separable convolution neural network (DS-CNN) to classify lung sounds accurately and efficiently. We extracted a total of three features for the shrunk DS-CNN model: the short-time Fourier-transformed (STFT) feature, the Mel-frequency cepstrum coefficient (MFCC) feature, and the fused features of these two. We observed that while DS-CNN models trained on either the STFT or the MFCC feature achieved an accuracy of 82.27% and 73.02%, respectively, fusing both features led to a higher accuracy of 85.74%. In addition, our method achieved 16 times higher inference speed on an edge device and only 0.45% less accuracy than RespireNet. This finding indicates that the fusion of the STFT and MFCC features and DS-CNN would be a model design for lightweight edge devices to achieve accurate AI-aided detection of lung diseases.

Highlights

The term lung sounds refers to “all respiratory sounds heard or detected over the chest wall or within the chest” [1]
This paper aims to propose a feature engineering process to extract the dedicated features for depthwise separable convolution neural network (DS-convolution neural network (CNN)) to classify four types of lung sounds: normal, continuous, discontinuous, and unknown
We have proposed a feature engineering process to extract dedicated features for the shrunk depthwise separable (DS)-CNN to classify four types of lung sounds

Summary

Introduction

The term lung sounds refers to “all respiratory sounds heard or detected over the chest wall or within the chest” [1]. Pulmonary conditions are diagnosed through lung auscultation, which refers to using a stethoscope for hearing a patient’s lung sounds. Lung auscultation can rapidly and safely rule out severe diseases and diagnose some pulmonary disorders’ flare-ups. A stethoscope has been an indispensable medical device for physicians to diagnose lung disorders for centuries. Recognizing the subtle distinctions among various lung sounds is an acquired skill that requires sufficient training and clinical experience. As COVID-19 sweeps the globe, lung auscultation still stays vital for monitoring confirmed cases [2]. Remote automatic auscultation systems may play a crucial role in lowering infection risks in medical workers. How artificial intelligence can be leveraged to assist physicians in performing auscultation remotely and accurately has become ever more imperative

Objectives

Methods

Discussion

Conclusion