## 1 Introduction  
The exponential spread of COVID-19 in the world poses substantial challenges for public health services. The disease, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), initially identified in December 2019 in Wuhan, China, causes respiratory tract infections and spreads rapidly through contagion between people, thus overwhelming health systems worldwide. It is necessary to evaluate the contagion scenarios and identify as many suspicious cases as possible to define appropriate isolation and treatment strategies [1,2]. Clinically, patients infected with SARS-CoV-2 present fever, cough, dyspnea, muscle aches, and bilateral pneumonia in imaging [3,4]. Even though studies suggest that the Omicron variant has a lower replication competence in human lung, thus reducing the pneumonia occurrence [5], mechanisms for screening and monitoring the evolution of the disease in the lungs are still essential, in the sense that we still do not know how the disease will evolve in the years to come. Imaging in chest radiography or computed tomography (CT) is the most common method to support the diagnosis of pneumonia in symptomatic patients [6]. There are clear recommendations from the WHO (World Health Organization) and the American Radiology Society for the use of imaging only in particular situations, and CT as part of the initial screening stage [7, 8, 9]. With the progression of the disease in the patient, characteristic chest radiographic patterns become more evident, which allows using X-ray images to support the disease diagnosis and follow-up. Even with limited resources, many public and private health systems have X-ray machines distributed throughout the country, which makes chest radiography an accessible, fast, and inexpensive alternative for diagnostic screening. In this scenario, an artificial intelligence (AI) system can be a tool to support radiologists or the medical staff directly in a suspected COVID-19 pneumonia patient, especially in areas where no radiology specialist is available [10], and in situations where there is a higher pressure on the health system from a higher demand caused by an epidemic or pandemic situation. There are many deep learning (DL) algorithms proposed in the literature to detect COVID-19 in radiographs, the majority based on popular convolutional neural networks (CNN) architectures for image classification, such as VGG, Inception, Xception, and Resnet. These algorithms take benefit from the DL characteristic of automatic feature extraction. Nevertheless, learning the features normally requires training the algorithms with a huge amount of annotated images. For a thorough review, please refer to [11,12]. It is difficult to categorize CXR images for COVID-19. The images have few semantic regions (sparsity) and other pulmonary infections generate similar lesions on the lungs, so there is also an inter-class similarity in the images. Recently, some studies that were based on the VGG-16 architecture proposed new methods to enhance feature extraction in CXR images. The work by [13] adopted a novel approach based on the bag of deep visual words (BoDVW) to classify CXR images. The method removes the feature map normalization step and adds the deep features normalization step on the raw feature maps, preserving the semantics of each feature map that might have importance to differentiating COVID-19 from other forms of pneumonia. This method was improved by [14], proposing a multi-scale BoDVW, exploiting three different scales of the pooling layer's output feature map from a VGG-16 model. The study by [15] used an attention module to capture the spatial relationship between the regions of interest in CXR images. The method produced a classification accuracy of 79.58% in the 3-class problem (COVID vs. No_findings vs. Pneumonia), 85.43% in the 4-class problem (COVID vs. Normal vs. Pneumonia bacteria vs. Pneumonia viral), and 87.49% in the 5-class problem (COVID vs. No_findings vs. Normal vs. Pneumonia bacteria vs. Pneumonia viral).