Detection of Covid-19 through Cough and Breathing Sounds using CNN

ABSTRACT

Covid-19 is declared a global pandemic by WHO due to its high infectivity rate. Medical attention is required to test and diagnose those with Covid-19 like symptoms. They are required to take an RT-PCR test which takes about 10-15 hours to obtain the result, and in some cases, it goes up to 3 days when the demand is too high. Majority of victims go unnoticed because they are not willing to get tested. The commonly used RT-PCR technique requires human contact to obtain the swab samples to be tested. Also, there is a shortage of testing kits in some areas and there is a need for self-diagnostic testing. This solution is a preliminary analysis. The basic idea is to use sound data, in this case, cough sounds, breathing sounds and speech sounds to isolate its characteristics and deduce if it belongs to a person who is infected or not, based on the trained model analysis. An Ensemble of Convolution neural networks have been used to classify the samples based on cough, breathing and speech samples, the model also considers symptoms exhibited by the person such as fever, cold, muscle pain etc. These Audio samples have been pre-processed and converted into Mel spectrograms and MFCC (Mel Cepstral Coefficients) are obtained that are fed as input to the model. The model gave an accuracy of 88.75% with a recall of 71.42 and Area Under Curve of 80.62%.