From Interpretable Filters to Predictions of Convolutional Neural Networks with Explainable Artificial Intelligence
This work addresses the need for interpretability in CNNs for medical diagnosis, specifically for Covid-19 detection, but it is incremental as it applies existing XAI methods to a new dataset.
The paper tackled the problem of interpreting convolutional neural networks (CNNs) as black boxes by applying explainable AI methods to analyze features for Covid-19 classification from dry cough spectrograms, resulting in highlighted important features and their relevance to classification.
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.