Automatic diagnosis of COVID-19 disease using deep convolutional neural network with multi-feature channel from respiratory sound data: Cough, voice, and breath

• Published in: Alexandria engineering journal Vol. 61; no. 2; pp. 1319 - 1334
• Main Authors: Lella, Kranthi Kumar, Pja, Alphonse
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.02.2022; THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University; Elsevier
• Subjects: Artificial Intelligence; COVID-19; Deep Convolutional Networks; Respiratory Sounds; COVID-19; Deep Convolutional Networks; Respiratory Sounds; Artificial Intelligence
• ISSN: 1110-0168; 1110-0168
• DOI: 10.1016/j.aej.2021.06.024

The problem of respiratory sound classification has received good attention from the clinical scientists and medical researcher’s community in the last year to the diagnosis of COVID-19 disease. The Artificial Intelligence (AI) based models deployed into the real-world to identify the COVID-19 disease from human-generated sounds such as voice/speech, dry cough, and breath. The CNN (Convolutional Neural Network) is used to solve many real-world problems with Artificial Intelligence (AI) based machines. We have proposed and implemented a multi-channeled Deep Convolutional Neural Network (DCNN) for automatic diagnosis of COVID-19 disease from human respiratory sounds like a voice, dry cough, and breath, and it will give better accuracy and performance than previous models. We have applied multi-feature channels such as the data De-noising Auto Encoder (DAE) technique, GFCC (Gamma-tone Frequency Cepstral Coefficients), and IMFCC (Improved Multi-frequency Cepstral Coefficients) methods on augmented data to extract the deep features for the input of the CNN. The proposed approach improves system performance to the diagnosis of COVID-19 disease and provides better results on the COVID-19 respiratory sound dataset.