Covid-19 detection using Cough Sound with Neural Networks
Parth Sharma, Purushottam Sharma, Vinod Kumar Shukla · 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) · 2022
COVID-19 has already had a significant influence on our everyday lives and with the influx of patients infected with the newer emerging variants there arises a need for a quick, accurate, and remote mode of identification. Cough sounds can play a vital role in the identification of COVID-19 in individuals. They can be used as an important factor to determine if the person is infected by COVID-19 or not, even with the prior existence of a respiratory ailment. Hence, we focused on providing a widely accessible and scalable solution through the method of a real-time mode of detection of the “COVID cough” via a machine learning model trained “COVID cough” recorded dataset. Based on the input, the person is provided with the diagnosis after being assessed by the model.