## 3 Results and Discussion
This conclusion somewhat limits the contribution of the work proposed by [PERSON] et al. [14]. In [44], the authors compared the performance of three CNN models: ResNet50, InceptionV3, and Inception ResNetV2. ResNet50 clearly outperformed the other two models. It attained a mean value for sensitivity of 96%, a mean value for specificity of 100%, and a mean value for \(F_{1}\) of 98%. These values are very similar to those obtained in this study. However, in this work, we used a total of 1037 images, with 200 images for the testing set, while they used a total of 100 images for both the training and testing sets. In [45], they pursued a four-category classification (Bacterial Pneumonia, Viral Pneumonia (other than COVID-19), Normal (uninfected), and COVID-19), but for the sake of this analysis, we mapped their results onto either COVID-19-positive or COVID-19-negative cases (including every case that was not COVID-19). Again, the obtained results are very similar to those obtained in this paper. However, those results were obtained on a dataset including 68 COVID-19 cases, with only eight of them in the testing set. Performing a similar comparison with [47] and grouping their classification results into COVID-19-positive and negative, a sensitivity of 91%, specificity of 99.5%, and \(F_{1}=94.8\%\) would be obtained. In [48], three different architectures of CNNs were analyzed for the detection of COVID-19, with the performance being evaluated in terms of accuracy, which is defined as  
\[\text{Accuracy}\triangleq\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+ \text{FP}+\text{FN}}.\]  
Figure 7: Evolution of the training of the Xception CNN. The accuracy values achieved by each architecture are 90%, 94.3%, and 96.8%. In [50; 51; 52], the obtained accuracy values were 97.78%, 95.5%, and 97%, respectively. All of these values are comparable to the ones presented in this work. The obtained accuracy levels herein are 97% for the VGG-16-based architecture and 98.4% for the Xception-based architecture. [PERSON] et al. [13] proposed a Bayesian classifier framework to classify chest X-ray images as infected or normal. They applied their proposed framework on three different datasets and achieved the highest accuracy (90.33%) using the largest dataset, called CXR-Pneumonia. This dataset contains 5856 images, of which 5216 images are used for training, 16 images for validation, and 624 images for testing. Their obtained accuracy is much lower than the accuracy we achieved using the Xception-based architecture (98.4%). Note that our model was trained using a much smaller training set. The results presented in this paper, as well as those presented in the papers cited, suggest that, with sufficient training, it is actually feasible to develop CNN-based classifiers with which to categorize PA and AP X-ray lung images as infected with COVID-19 or not infected. This is one of the most relevant conclusions that should be drawn from this work. Note that the obtained classifiers depend upon the labeled images used for training. Images of people infected with COVID-19 in the very early stages of the disease might have been labeled as negative despite actually being positive. Cases such as these can therefore be misclassified as negative. Hence, it is important to point out that the high potential of these methods resides in their ability to detect cases where the condition of the lungs affected by the disease is such that the pulmonary images exhibit visual characteristics typical for COVID-19-positive cases. Finally, it is worth mentioning that, once the model was trained, the time it took to classify ten images in a row was measured as approximately 270 milliseconds on a laptop with 16 GB of RAM and an Intel Core i7 vPro 8 th Generation CPU. Hence, considering the infrastructure required for running these types of models, this shows that our method is feasible and affordable for any medical institution. Our annotated codes can be found at [68].