## 2 Materials and Methods
Figures 3 and 4 represent the datasets distributions. Figure 1: Cimatec_Xcov19 workflow of DL models for COVID-19 classification. Figure 2: (**a**) Inception block formed by four convolutional trajectories for the same input. (**b**) General structure of the network with all the elements. Reprinted with permission from Ref. [26]. 2021, Andř  
There were 2917 images tagged as COVID-19 (6.7%). This is one of the largest collections of images used to train COVID-19 classifiers, to our knowledge. Before inputting the data into the models, we pre-processed the images for normalization and better feature extraction. A data augmentation process included new images with variations in the gamma contrast, which generated, in total, 132,093 images. We randomly distributed the dataset to 70% for training, 20% for validation, and 10% for testing, keeping the same distribution of classes from the original dataset. We chose to use a hold-out test dataset instead of doing cross-validation, due to hardware and time constraints. After building a stable system by training and testing it in the general dataset, we did an external validation with a new dataset of CXR from a Brazilian hospital focused on elder people and used explainable AI techniques to show how the algorithms are taking their classification decisions. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Description** & **\# of CXR** \\ \hline \multirow{3}{*}{RSNA Pneumonia Detection Challenge [27]} & Images labeled by the Society for Thoracic Radiology and MD.ai & \multirow{3}{*}{25,497} \\  & for pneumonia cases found in the chest radiograph database & \\  & made public by the National Institutes of Health (NIH). & \\ \hline \multirow{3}{*}{BIMCV PadChest [28]} & Digital Medical Image Bank of the Valencian Community. & \multirow{3}{*}{14,252} \\  & Images were interpreted and reported by radiologists at & \\  & Hospital San Juan (Spain) from 2009 to 2017. & \\ \hline \multirow{3}{*}{BIMCV COVID-19 [29]} & Digital Medical Image Bank of the Valencian Community & \multirow{3}{*}{1762} \\  & related to COVID-19 cases. & \\ \hline \multirow{3}{*}{HM Hospitales} & CXR images from patients from the HM Hospitals group in & \multirow{3}{*}{1277} \\  & different cities in Spain. Private Dataset. & \\ \hline \multirow{3}{*}{COVID-19 Image Data Collection [30]} & Data was collected from public sources, as well as through & \multirow{3}{*}{613} \\  & indirect collection from hospitals and doctors organized by a & \\ \cline{1-1}  & researcher from the University of Montreal. & \\ \hline \multirow{3}{*}{HC USP Competition} & Images obtained from patients from the HC hospital in São & \multirow{3}{*}{593} \\  & Paulo used for a competition. Private Dataset. & \\ \hline \multirow{3}{*}{Hospital Santa Izabel} & Images interpreted and reported by radiologists at Hospital & \multirow{3}{*}{37} \\  & Santa Izabel, Salvador, Baha, Brazil. Private Dataset. & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Datasets description and number of images. Figure 3: Dataset breakdown. During the development of the algorithms, we used one shared computing node with four Nvidia GPUs V100 with 32 MB of memory for each.