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

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. Results As a result, around 4% accuracy is achieved than traditional MFCC. We have classified COVID-19 sounds, asthma sounds, and regular healthy sounds using a 1D CNN classifier and shown around 90% accuracy to detect the COVID-19 disease from respiratory sounds. Conclusion A Data De-noising Auto Encoder (DDAE) was adopted to extract the acoustic sound signals in-depth features instead of traditional MFCC. The proposed model improves efficiently to classify COVID-19 sounds for detecting COVID-19 positive symptoms.

COVID-19 disease diagnosis with light-weight CNN using modified MFCC and enhanced GFCC from human respiratory sounds

“… Speech, Cough, and Breath 79 DTL MC [ 4 ] Android App developed for COVID-19 sound samples. Speech and Cough 88 SVM with PCA [ 24 ] Crowdsourced COVID-19 Sounds Dataset Speech, Cough, and Breath Task-1: 80 Task-2: 82 Task-3: 80 VGG Net with Augmentation [ 24 ] Crowdsourced COVID-19 Sounds Dataset Voice, Cough, and Breath Task-2: 87 Task-3: 88 1DCNN with DDAE [ 34 ] Crowdsourced COVID-19 Sounds Dataset Voice, Cough, and Breath Task - 1: 90 Task-2: 88 Task-3: 88 Task-4: 84 Task-5: 86 Light-Weight CNN with MMFCC (Proposed) Crowdsourced COVID-19 Sounds Dataset Voice, Cough, and Breath Class-1: 89.78 Class-2: 92.32 Class-3: 89.69 Class-4: 88.74 …”

Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic

“…These sounds were collected using website applications to enable sound-based diagnosis for COVID-19. In [ 81 ], Dunne et al utilized three different datasets for diagnosis, including (1) Google’s Audioset ( ) (Last access date: 17 February 2021) aggregated from YouTube videos (non-COVID-19); (2) the Corswara dataset (COVID-19); and (3) data collected at Stanford University ( ) (Last access date: 17 February 2021). In [ 82 ], the authors developed a mobile application that analyzed the patient’s cough sound and provided COVID-19 identification within 2 min.…”

Data-Driven Analytics Leveraging Artificial Intelligence in the Era of COVID-19: An Insightful Review of Recent Developments

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