Automatic COVID-19 disease diagnosis using 1D convolutional neural network and augmentation with human respiratory sound based on parameters: cough, breath, and voice

TL;DR

This paper presents a novel 1D CNN approach combined with data augmentation and deep feature extraction to improve COVID-19 diagnosis accuracy from respiratory sounds like cough, breath, and voice.

Contribution

It introduces a 1D CNN model with data augmentation and DDAE-based deep feature extraction for COVID-19 detection from respiratory sounds, outperforming previous models.

Findings

Enhanced accuracy with DDAE features

Effective augmentation improves dataset robustness

Outperforms existing models in COVID-19 sound classification

Abstract

The issue in respiratory sound classification has attained good attention from the clinical scientists and medical researcher's group in the last year to diagnosing COVID-19 disease. To date, various models of Artificial Intelligence (AI) entered into the real-world to detect the COVID-19 disease from human-generated sounds such as voice/speech, cough, and breath. The Convolutional Neural Network (CNN) model is implemented for solving a lot of real-world problems on machines based on Artificial Intelligence (AI). In this context, one dimension (1D) CNN is suggested and implemented to diagnose respiratory diseases of COVID-19 from human respiratory sounds such as a voice, cough, and breath. An augmentation-based mechanism is applied to improve the preprocessing performance of the COVID-19 sounds dataset and to automate COVID-19 disease diagnosis using the 1D convolutional network. Furthermore, a DDAE (Data De-noising Auto Encoder) technique is used to generate deep sound features such as the input function to the 1D CNN instead of adopting the standard input of MFCC (Mel-frequency cepstral coefficient), and it is performed better accuracy and performance than previous models.