Abnormal Respiratory Sounds Classification Using Deep CNN Through Artificial Noise Addition.

Rizwana Zulfiqar,Fiaz Majeed,Rizwana Irfan,Elhadj Benkhelifa,Abdelkader Nasreddine Belkacem,Hafiz Tayyab Rauf

doi:10.3389/fmed.2021.714811

Abstract

Respiratory sound (RS) attributes and their analyses structure a fundamental piece of pneumonic pathology, and it gives symptomatic data regarding a patient's lung. A couple of decades back, doctors depended on their hearing to distinguish symptomatic signs in lung audios by utilizing the typical stethoscope, which is usually considered a cheap and secure method for examining the patients. Lung disease is the third most ordinary cause of death worldwide, so; it is essential to classify the RS abnormality accurately to overcome the death rate. In this research, we have applied Fourier analysis for the visual inspection of abnormal respiratory sounds. Spectrum analysis was done through Artificial Noise Addition (ANA) in conjunction with different deep convolutional neural networks (CNN) to classify the seven abnormal respiratory sounds—both continuous (CAS) and discontinuous (DAS). The proposed framework contains an adaptive mechanism of adding a similar type of noise to unhealthy respiratory sounds. ANA makes sound features enough reach to be identified more accurately than the respiratory sounds without ANA. The obtained results using the proposed framework are superior to previous techniques since we simultaneously considered the seven different abnormal respiratory sound classes.

Highlights

Respiratory sound (RS) attributes and their analyses structure a fundamental piece of pneumonic pathology such as COVID-19 pneumonia, and it gives symptomatic data about a patient’s lung
Researchers use datasets from multiple repositories for research purposes, and all of them were primarily generated for academic aspire
As we talk about the accuracy results, the overall accuracy of VGG-B1 for all abnormal RS classes is traced as 0.95%

Summary

Introduction

Respiratory sound (RS) attributes and their analyses structure a fundamental piece of pneumonic pathology such as COVID-19 pneumonia, and it gives symptomatic data about a patient’s lung. A couple of decades back, doctors depended on their hearing to distinguish symptomatic signs in lung audios through utilizing the standard stethoscope equipment. The typical stethoscope is usually considered a cheap and secure method for examining the patients, other than setting aside less effort required for the conclusion. It gives much data about the respiratory organ and the indications of the sicknesses that influence it [1, 2]. With the guide of electronic stethoscopes combined with pattern recognition and artificial intelligence, the mechanized respiratory sound examination has drawn much consideration since it conquers the confinements of normal auscultation and gives an effective technique to clinical conclusion [3]. Machine Learning [4] and Deep learning approaches play an essential role in health care [5] and industrial applications [6, 7] for prediction and optimization

Objectives

Methods

Results

Conclusion