A comparative analysis of deep neural network architectures for the dynamic diagnosis of COVID-19 based on acoustic cough features.

Abstract

The study aims to assess the detection performance of a rapid primary screening technique for COVID‐19 that is purely based on the cough sound extracted from 2200 clinically validated samples using laboratory molecular testing (1100 COVID‐19 negative and 1100 COVID‐19 positive). Results and severity of samples based on quantitative RT‐PCR (qRT‐PCR), cycle threshold, and patient lymphocyte numbers were clinically labeled. Our suggested general methods consist of a tensor based on audio characteristics and deep‐artificial neural network classification with deep cough convolutional layers, based on the dilated temporal convolution neural network (DTCN). DTCN has approximately 76% accuracy, 73.12% in TCN, and 72.11% in CNN‐LSTM which have been trained at a learning rate of 0.2%, respectively. In our scenario, CNN‐LSTM can no longer be employed for COVID‐19 predictions, as they would generally offer questionable forecasts. In the previous stage, we discussed the exactness of the total cases of TCN, dilated TCN, and CNN‐LSTM models which were truly predicted. Our proposed technique to identify COVID‐19 can be considered as a robust and in‐demand technique to rapidly detect the infection. We believe it can considerably hinder the COVID‐19 pandemic worldwide.