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

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 disea...

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Autores principales:	Kranthi Kumar Lella, Alphonse Pja
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2022
Materias:	Artificial Intelligence Deep Convolutional Networks COVID-19 Respiratory Sounds Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/9c9968a2b5af41ddb9e6ef3a2fe26217
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spelling	oai:doaj.org-article:9c9968a2b5af41ddb9e6ef3a2fe262172021-11-18T04:45:15ZAutomatic diagnosis of COVID-19 disease using deep convolutional neural network with multi-feature channel from respiratory sound data: Cough, voice, and breath1110-016810.1016/j.aej.2021.06.024https://doaj.org/article/9c9968a2b5af41ddb9e6ef3a2fe262172022-02-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1110016821003859https://doaj.org/toc/1110-0168The 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.Kranthi Kumar LellaAlphonse PjaElsevierarticleArtificial IntelligenceDeep Convolutional NetworksCOVID-19Respiratory SoundsEngineering (General). Civil engineering (General)TA1-2040ENAlexandria Engineering Journal, Vol 61, Iss 2, Pp 1319-1334 (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	Artificial Intelligence Deep Convolutional Networks COVID-19 Respiratory Sounds Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Artificial Intelligence Deep Convolutional Networks COVID-19 Respiratory Sounds Engineering (General). Civil engineering (General) TA1-2040 Kranthi Kumar Lella Alphonse Pja Automatic diagnosis of COVID-19 disease using deep convolutional neural network with multi-feature channel from respiratory sound data: Cough, voice, and breath
description	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.
format	article
author	Kranthi Kumar Lella Alphonse Pja
author_facet	Kranthi Kumar Lella Alphonse Pja
author_sort	Kranthi Kumar Lella
title	Automatic diagnosis of COVID-19 disease using deep convolutional neural network with multi-feature channel from respiratory sound data: Cough, voice, and breath
title_short	Automatic diagnosis of COVID-19 disease using deep convolutional neural network with multi-feature channel from respiratory sound data: Cough, voice, and breath
title_full	Automatic diagnosis of COVID-19 disease using deep convolutional neural network with multi-feature channel from respiratory sound data: Cough, voice, and breath
title_fullStr	Automatic diagnosis of COVID-19 disease using deep convolutional neural network with multi-feature channel from respiratory sound data: Cough, voice, and breath
title_full_unstemmed	Automatic diagnosis of COVID-19 disease using deep convolutional neural network with multi-feature channel from respiratory sound data: Cough, voice, and breath
title_sort	automatic diagnosis of covid-19 disease using deep convolutional neural network with multi-feature channel from respiratory sound data: cough, voice, and breath
publisher	Elsevier
publishDate	2022
url	https://doaj.org/article/9c9968a2b5af41ddb9e6ef3a2fe26217
work_keys_str_mv	AT kranthikumarlella automaticdiagnosisofcovid19diseaseusingdeepconvolutionalneuralnetworkwithmultifeaturechannelfromrespiratorysounddatacoughvoiceandbreath AT alphonsepja automaticdiagnosisofcovid19diseaseusingdeepconvolutionalneuralnetworkwithmultifeaturechannelfromrespiratorysounddatacoughvoiceandbreath
_version_	1718425056134037504

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

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