COVID-19 Detection System using Recurrent Neural Networks

Abstract

Lately, an immense amount of work has been done by people working on the frontlines, such as hospitals, clinics, and laboratories, alongside researchers and scientists who are also making considerable efforts in the fight against the COVID-19 epidemic. Due to the unconscionable dissemination of the disease, the implementation of Artificial Intelligence (AI) has made a significant contribution to the digital health district by applying the basics of Automatic Speech Recognition (ASR) and deep learning algorithms. In this study, we highlight the importance of speech signal processing in the process of early screening and diagnosing the COVID-19 virus by utilizing the Recurrent Neural Network (RNN) and specifically its significant well-known architecture, the Long Short-Term Memory (LSTM) for analyzing the acoustic features of cough, breathing, and voice of the patients. Our results show a low accuracy in the voice test compared to both coughing and breathing sound samples. Moreover, our results are preparatory, and there is a possibility to enhance the accuracy of the voice tests by expanding the data set and targeting a larger group of healthy and infected people.