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Convolutional neural networks (CNN) are known for their excellent featureextraction capabilities to enable the learning of models from data, yet areused as black boxes. An interpretation of the convolutional filtres andassociated features can help to establish an understanding of CNN todistinguish various classes. In this work, we focus on the explainability of aCNN model called as cnnexplain that is used for Covid-19 and non-Covid-19classification with a focus on the interpretability of features by theconvolutional 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 provideinterpretation of convolutional filtres, and relevant features, and their rolein classification. We have analyzed the explanation of these methods forCovid-19 detection using dry cough spectrograms. Explanation results obtainedfrom the LIME, SmoothGrad, and Grad-CAM highlight important features ofdifferent spectrograms and their relevance to classification.

From Interpretable Filters to Predictions of Convolutional Neural Networks with Explainable Artificial Intelligence