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

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.