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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-02-01
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| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016821003859 |
| _version_ | 1819002336831340544 |
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| author | Kranthi Kumar Lella Alphonse Pja |
| author_facet | Kranthi Kumar Lella Alphonse Pja |
| author_sort | Kranthi Kumar Lella |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-20T23:03:30Z |
| format | Article |
| id | doaj.art-9c9968a2b5af41ddb9e6ef3a2fe26217 |
| institution | Directory Open Access Journal |
| issn | 1110-0168 |
| language | English |
| last_indexed | 2024-12-20T23:03:30Z |
| publishDate | 2022-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj.art-9c9968a2b5af41ddb9e6ef3a2fe262172022-12-21T19:23:56ZengElsevierAlexandria Engineering Journal1110-01682022-02-0161213191334Automatic diagnosis of COVID-19 disease using deep convolutional neural network with multi-feature channel from respiratory sound data: Cough, voice, and breathKranthi Kumar Lella0Alphonse Pja1Corresponding Author at: Department of Computer Applications, NIT Tiruchirappalli, Tamil Nadu - 620015, India.; Department of Computer Applications, NIT Tiruchirappalli, Tamil Nadu 620015, IndiaDepartment of Computer Applications, NIT Tiruchirappalli, Tamil Nadu 620015, IndiaThe 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.http://www.sciencedirect.com/science/article/pii/S1110016821003859Artificial IntelligenceDeep Convolutional NetworksCOVID-19Respiratory Sounds |
| spellingShingle | 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 Alexandria Engineering Journal Artificial Intelligence Deep Convolutional Networks COVID-19 Respiratory Sounds |
| 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_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_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_sort | automatic diagnosis of covid 19 disease using deep convolutional neural network with multi feature channel from respiratory sound data cough voice and breath |
| topic | Artificial Intelligence Deep Convolutional Networks COVID-19 Respiratory Sounds |
| url | http://www.sciencedirect.com/science/article/pii/S1110016821003859 |
| work_keys_str_mv | AT kranthikumarlella automaticdiagnosisofcovid19diseaseusingdeepconvolutionalneuralnetworkwithmultifeaturechannelfromrespiratorysounddatacoughvoiceandbreath AT alphonsepja automaticdiagnosisofcovid19diseaseusingdeepconvolutionalneuralnetworkwithmultifeaturechannelfromrespiratorysounddatacoughvoiceandbreath |