  The present work proposes a deep-learning-based approach for the
classification of COVID-19 coughs from non-COVID-19 coughs and that can be used
as a low-resource-based tool for early detection of the onset of such
respiratory diseases. The proposed system uses the ResNet-50 architecture, a
popularly known Convolutional Neural Network (CNN) for image recognition tasks,
fed with the log-Mel spectrums of the audio data to discriminate between the
two types of coughs. For the training and validation of the proposed deep
learning model, this work utilizes the Track-1 dataset provided by the DiCOVA
Challenge 2021 organizers. Additionally, to increase the number of
COVID-positive samples and to enhance variability in the training data, it has
also utilized a large open-source database of COVID-19 coughs collected by the
EPFL CoughVid team. Our developed model has achieved an average validation AUC
of 98.88%. Also, applying this model on the Blind Test Set released by the
DiCOVA Challenge, the system has achieved a Test AUC of 75.91%, Test
Specificity of 62.50%, and Test Sensitivity of 80.49%. Consequently, this
submission has secured 16th position in the DiCOVA Challenge 2021 leader-board.
