Predicting COVID-19 Cough Sounds Using Spectrogram Analysis Across Multiple Classes

Abstract

The COVID-19 pandemic has underscored the need for effective diagnostic tools. One promising avenue involves analyzing cough sounds to glean insights into respiratory health. This study presents a new method for predicting COVID-19 cough sounds using spectrogram analysis across various classes. We leverage advanced deep learning models such as DenseNet121, VGG16, ResNet50, and Inception Net, alongside our novel CNN architecture, to extract pertinent features from cough sound spectrograms. We use a diverse dataset encompassing cough sounds from COVID-19 positive and negative cases, as well as other respiratory conditions, for model training and assessment. Our results demonstrate the effectiveness of our approach in accurately categorizing COVID-19 cough sounds, outperforming existing models. This methodology shows promise as a non-invasive, scalable, and economical tool for early COVID-19 detection and monitoring, aiding public health efforts during the pandemic.