## 1 Introduction
#### Related Work
[45] also used a ResNet50 CNN trained with the COVID-Net dataset [26] to distinguish COVID-19 from bacterial pneumonia, non-COVID-19 viral pneumonia, and normal cases. Since the original set had only 68 COVID-19-positive images, they had to perform significant data augmentation. Due to the scarcity of available images, in [46] a capsule network model is proposed for examining lung RX images. In [47], the authors developed a CNN architecture that leverages the projection-expansion-extension design. [PERSON] et al. [48] implemented three CNN-based classifiers for pulmonary radiographies by using VGG16, ResNet50, and EfficientNetB0 as backbones. In [49], based on the pre-trained AlexNet CNN, a relief feature selection mechanism is implemented that is then used with a support vector machine to perform the classification. [PERSON] et al. [50] proposed a CNN, named OptCoNet, whose hyperparameters were optimized using the grey wolf optimizer (GWO) algorithm. In [51], a ResNet CNN architecture was utilized to derive three different classifiers, which were then assembled to obtain a more accurate classifier. [PERSON] et al. [52] compared three architectures of CNNs for the classification of pulmonary radiographies into three classes: normal, COVID-19, and other pneumonia. In this work, we collected images from all the relevant sources that we could find [26; 28; 29; 53; 54; 55]. Since the number of available images has been continually increasing, we have added more and more images to our model but also had to discard a significant number of them due to constraints that will be elaborated on in Section 2. Pneumonia can occur due to several different causes. It can be caused by bacteria or viruses, and there are even multiple types of viral pneumonia. Hence, we intended to train our model to distinguish cases of COVID-19 from cases of any other viral or bacterial disease. Consequently, we had to procure a set of images that, in addition to images of COVID-19 infection, also included images of bacterial infections, viral infections (other than COVID-19), and no infection. We collected images from all relevant open access sources that we found until November 2020. These sources can be found in [26; 53; 54; 55] and were used among many other similar publications [12; 13; 14; 48; 52; 51; 50; 52]. It is worth mentioning that, in general, these efforts are still in the research stage and not yet intended as production models ready for clinical diagnosis. In order to attain that level, many more properly labeled images should be made available for training and testing. We are working continuously to improve them as new data become available. However, the main goal of this work was to illustrate the process and feasibility of developing serious classifiers of X-ray images to indicate the existence of COVID-19 infection. Another goal of this work was to compare the performance of two renowned CNN architectures, VGG16 [56] and Xception [57]. The main contribution of this article consists of the description of two CNN-based classifiers that can distinguish pulmonary radiographies that include COVID-19-infected lungs from those that do not. Our novel classifiers exhibit a performance comparable to that found in other similar state-of-the-art studies [12; 13; 14; 48; 52; 51; 50; 52]. In fact, our classifiers even slightly outperform some of these others in certain metrics. The remainder of this paper is structured as follows. Section 2 describes the image collection and preparation processes as well as the architecture of the convolutional neural network implemented for the classifier. Section 3 elaborates on the obtained results and discusses potential directions for improvement. Finally, Section 4 presents our main highlights and observations about this work.