###### Abstract  
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. These popular and well-documented successes use extremely deep CNNs such as VGG16, DenseNet121, and Xception. However, these well-known deep learning models use tens of millions of parameters based on a large number of pretrained filters that have been repurposed from previous data sets. Among these identified filters, a large portion contain no information yet remain as input features. Thus far, there is no effective method to omit these noisy features from a data set, and their existence negatively impacts prediction performance. In this paper, a novel interaction-based convolutional neural network (ICNN) is introduced that does not make assumptions about the relevance of local information. Instead, a model-free influence score (I-score) is proposed to directly extract the influential information from images to form important variable modules. This innovative technique replaces all pretrained filters found by trial-and-error with explainable, influential, and predictive variable sets (modules) determined by the I-score. In other words, future researchers need not rely on pretrained filters; the suggested algorithm identifies only the variables or pixels with high I-score values that are extremely predictive and important. The proposed method and algorithm were tested on real-world data set and a state-of-the-art prediction performance of 99.8% was achieved without sacrificing the explanatory power of the model. This proposed design can efficiently screen patients infected by COVID-19 before human diagnosis and can be a benchmark for addressing future XAI problems in large-scale data sets. explaibable artificial intelligence; convolutional neural networks; deep learning; Chest X-ray Image; I-score +
Footnote †: journal: Computer Vision and Pattern Recognition  
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Footnote 1: email: [EMAIL_ADDRESS]  
nasal swab most readers will be familiar with), but the accuracy of this test suffers from significantly occurring false negatives [1]. According to a study conducted early in the pandemic consisting of 1014 patients in Wuhan, China [5], the authors concluded that chest CT has a high sensitivity for diagnosis of COVID-19. Moreover, chest CT may be considered a primary tool for the current COVID-19 detection in epidemic areas [5]. This innovation, if used earlier, could have revolutionized the initial screening procedure of COVID-19 and might have prevented many unnecessary deaths. Looking forward, we theorize that other related diseases could use similar detection methods to prevent future epidemics and pandemics. Creating these methods now will ensure the development of testing procedures with the speed, accuracy, and explainability that will be required to deploy such artificial intelligence (AI) systems into future testing environments. AI-centered medical-imaging-based deep learning systems have already been developed for image feature extraction, including shape and spatial relation features. Specifically, the convolutional neural network (CNN) has shown promising results in feature extraction and learning [3]. However, traditional CNNs heavily rely on pretrained filters (also known as kernels) that are designed to extract certain features from images, and are trained with past data sets that may or may not be relevant to the data at hand [6; 7; 8]. This traditional design has little ability to extract only the useful information, and creates noisy features that must be sifted. CNN models have many convolutional layers, and each convolutional layer uses many pretrained filters with no feature selection methods. Hence, any and all information created using pretrained filters, noisy or not, is included in those convolutional layers for later analysis.