  The field of Explainable Artificial Intelligence (XAI) aims to build
explainable and interpretable machine learning (or deep learning) methods
without sacrificing prediction performance. Convolutional Neural Networks
(CNNs) have been successful in making predictions, especially in image
classification. However, these famous deep learning models use tens of millions
of parameters based on a large number of pre-trained filters which have been
repurposed from previous data sets. We propose a novel Interaction-based
Convolutional Neural Network (ICNN) that does not make assumptions about the
relevance of local information. Instead, we use a model-free Influence Score
(I-score) to directly extract the influential information from images to form
important variable modules. We demonstrate that the proposed method produces
state-of-the-art prediction performance of 99.8% on a real-world data set
classifying COVID-19 Chest X-ray images without sacrificing the explanatory
power of the model. This proposed design can efficiently screen COVID-19
patients before human diagnosis, and will be the benchmark for addressing
future XAI problems in large-scale data sets.
