## 2 Materials and Methods  
In this work, we present Cimatec_XCOV19, a deep learning system to support the detection of COVID-19 in radiographs. The system is composed of two AI models: one evaluates normal and abnormal examinations, while the second is a binary classifier for being suggestive of COVID-19 or not. Both models are variations of Inception-V3 CNNs [25] trained with pre-processed CXR. Figure 1 shows the system workflow for the evaluation of an image. A CXR image, X, is pre-processed and serves as input for both models \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Objective** & **Base Model** & \
\begin{tabular}{c} **Training Dataset** \\ **(\# of CXR)** \\ \end{tabular} &
\begin{tabular}{c} **External Validation** \\ **Dataset (\# of CXR)** \\ \end{tabular} \\ \hline
[19] & Detect common thoracic disease & DenseNet-121 & 120,702 & 24,500 \\
[19] & Diagnose COVID-19 and multiclass classification & DenseNet-121 & 27,825/1571 [1] & China 1034 \\  & & & & Ecuador 650/132 [1] \\
[20] & Detect COVID-19 pneumonia & \
\begin{tabular}{c} Ensemble of CNN: \\ Densenet-121, Resnet-50, \\ Inception, Inception-Resnet, \\ Xception, EfficientNet-B2 \\ and progression \\ \end{tabular} &
\begin{tabular}{c} Pre-training: NIH-CXR14 \\ dataset +100,000 \\ Fine-tunning: 14,788/4253 [1] \\ \end{tabular} & 2214 images/1192 [1] \\
[21] & Predict COVID-19 severity and progression & VGG-11 and EfficientNet-B0 & 1834 all COVID-19 patients & 475 \\
[22] & Detect COVID-19 cases & COVID-Net CNN & 13,975/358 [1] & 300/100 [1] \\
[23] & Detect COVID-19 (3 binary classifiers) & ResNet-50 & 7406/341 [1] & N/A [2] \\
[24] & Detect COVID-19 and Multiclass Classification & DarkNet-19 & 1125/125 [1] & N/A [2] \\ This work & Detect COVID-19 & Inception-V3 & 44,031/2917 [1] & 1158/13 [1] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A partial list of DL algorithms based on COVID-19 radiographs with publicly available code. simultaneously. The system evaluates the input image in both CNN independently. They have different box colors in the figure. One model evaluates the probability of image X being abnormal, P\({}_{\text{abn}}\)(X), while the other evaluates the probability of image X being COVID-19, P\({}_{\text{cov}}\)(X). An outcome suggestive of COVID-19 occurs only when the multiplication of the outputs of the two models is greater than 0.5. Deep CNNs are often large models and demand much computational power. The widely used Inception-V3 architecture is made of suitably factorized convolutions and aggressive regularization to scale up the networks to efficiently use the available processing capabilities. The model has both symmetric and asymmetric building blocks comprising convolutions layers, average and max pooling operations, concatenation, and fully connected layers. The model uses dropout layers and batch normalization applied to activation inputs. The loss function is a softmax. The Inception architecture innovation is the implementation of inception blocks, which splits the input into different parallel trajectories. There is a concatenation module at the end of the inception blocks to integrate these different paths, as observed in Figure 2. The Supplementary Materials details our network's architecture, showing the structures in block diagrams. It is possible to notice the modifications they have from a traditional Inception-V3 network. The dataset was prepared by collecting 44,031 examinations from different sources, mainly from public databases and Brazilian and Spanish healthcare institutions. We did a visual inspection of each database and manually excluded out-of-the-context images and those with bad quality. Table 2, below, details the origins of the datasets. There were multiple image classifications methods in the datasets. The image tags changed according to the origin of the data. For proper use by the models, we reclassified the CXR labels into three categories: (i) normal, (ii) abnormal, but not COVID-19, and (iii) abnormal, and suggestive of COVID-19.