  Convolutional neural networks (CNN) are known for their excellent feature
extraction capabilities to enable the learning of models from data, yet are
used as black boxes. An interpretation of the convolutional filtres and
associated features can help to establish an understanding of CNN to
distinguish various classes. In this work, we focus on the explainability of a
CNN model called as cnnexplain that is used for Covid-19 and non-Covid-19
classification with a focus on the interpretability of features by the
convolutional filters, and how these features contribute to classification.
Specifically, we have used various explainable artificial intelligence (XAI)
methods, such as visualizations, SmoothGrad, Grad-CAM, and LIME to provide
interpretation of convolutional filtres, and relevant features, and their role
in classification. We have analyzed the explanation of these methods for
Covid-19 detection using dry cough spectrograms. Explanation results obtained
from the LIME, SmoothGrad, and Grad-CAM highlight important features of
different spectrograms and their relevance to classification.
