## 1 Introduction
#### Related Work  
There have recently been many remarkable efforts to exploit machine learning tools to facilitate the analysis of images for the diagnosis of COVID-19. These efforts include initiatives to collect worldwide images and make them public [26; 27; 28; 29; 30] as well as attempts to automate the analysis of CT scans or lung X-ray images. [PERSON] et al. [15] developed a method for automatically evaluating lung conditions using CT scans to determine COVID-19 infection. Their method is based on individually optimized Hounsfield unit (HU) thresholds. [PERSON] et al. [16] investigated the practicality of using deep learning models trained on chest CT images to detect COVID-19 efficiently. In [31], the authors offer a review of techniques from the image processing field that are being used extensively to identify COVID-19 in CT and RX images. [PERSON] et al. [32] employed machine learning tools to predict the infectivity of COVID-19. In [33], a forecasting model for predicting the spread of COVID-19 is proposed. The studies [34; 35; 36] focus on training convolutional neural networks (CNNs) to detect COVID-19 in CT chest scans. Artificial neural networks (ANNs) are biologically inspired algorithms that mimic the computational aspects of the human brain. They contain multiple connected computational units (resembling individual neurons) that transmit and receive information according to specific rules, rules that dictate the operation modes of the networks. CNNs are one type of ANN that have been proven to be very powerful for computer vision applications [37; 38]. In recent years, a new class of ANNs named spiking neural networks (SNNs) has also been intensively investigated because of their promising computational capabilities [39; 40]. However, the generic and effective training methods for these networks still remain cumbersome and, therefore, their fields of application are much more specific than those of their predecessors. CNN algorithms have the advantage that they can be run very efficiently on standard computers that are equipped with graphics processing units, without the need to use dedicated platforms based on neuromorphic systems [41; 42]. Generally speaking, CT scans can be more accurate for revealing lung conditions and diagnosing related diseases than RX images. However, CT scans can be almost one order of magnitude more expensive than their RX counterparts, and they also take much longer complete (about 40 min) than X-ray imaging, which can be completed in about 5 min. Moreover, CT scanners are scarce in certain regions of the world, whereas radiography systems are much more common [43]. Therefore, there is also great motivation to explore the feasibility of automatically identifying COVID-19 in RX images. The main aim of this work was to contribute evidence that supports the following hypothesis: _Deep learning can be used to recognize COVID-19 infections from lung X-ray images_. [PERSON] et al. [12] developed a deep-learning-based system called COVID-XNet, which preprocesses chest X-ray images and then classifies them as normal or COVID-19-infected. [PERSON] et al. [13] proposed using a Bayesian classifier framework to classify chest X-ray images as either infected or normal. Features are extracted from segmented lungs and then modeled using a proposed mixture model; finally, the Bayesian classifier is applied to perform the classification. [PERSON] et al. [14] combined/fused two types of features (early fusion) and then used them to train a VGGNet classifier for the detection of COVID-19 from chest X-ray images. The fused features were extracted from X-ray images using a histogram-oriented gradient (HOG) and convolutional neural networks (CNNs). [PERSON] et al. [44] analyzed the performance of three CNN models--ResNet50, InceptionV3, and Inception ResNetV2--for detecting COVID-19 from RX pulmonary images. However, only 50 COVID-19-positive and 50 COVID-19-negative images were used in the study. [PERSON] et al.