## 4 Conclusions  
In this work, the authors have presented a novel CAD system, COVID-XNet, for detecting COVID-19 in frontal chest X-ray images. The system, which consists of a custom CNN, was trained and validated from scratch with X-ray images that were obtained from publicly-available datasets. These images were preprocessed with different methods (histogram matching, rib suppression, and CLAHE) in order to enhance the relevant information. Using a five-fold cross-validation scheme, COVID-XNet achieved 92.53% sensitivity, 96.33% specificity, 93.76% precision, 93.14% F1-score, 94.43% balanced accuracy, and an AUC value of 0.988 on average over the different folds.  
CAMs were used to visualize the output of the CNN, where the relevant features that the system considered for COVID-19 detection were highlighted. The obtained heatmaps were compared and verified with their corresponding ground truths from the radiologists that diagnosed these cases, and were also validated by a lung specialist.  
In this work, we combined X-ray images from different publicly-available sources in order to obtain an updated dataset with a larger quantity of images than other state-of-the-art works, making the system more robust, since a greater variability of cases were explored when training and validating the model. In addition, as this work is proposed as a supporting tool to aid specialists in the screening process, we believe that it is important to verify the results with a ground truth. For this reason, a lung specialist supervised and validated our work, which is an aspect that most state-of-the-art works did not consider.  
The proposed system could be useful as a screening test for COVID-19 diagnosis in combination with patients' clinical manifestations and/or laboratory results to discard severe cases and decide whether the patient should be hospitalized. The performance of the system when predicting new unseen images shows that the model generalizes well, proving that COVID-XNet could be the first step for developing a universal CAD system for COVID-19 diagnosis in X-ray images.  
This work, by no means, presents a solution that is currently ready for its production phase. More tests and improvements should be performed before considering the use of any deep learning solution in hospitals. COVID-XNet was never conceived as a replacement for human radiologists, but as a tool to aid them and contribute to the fight against COVID-19. Future research will focus on training and testing the model with more images, since, currently, only a few small datasets are available.  
**Author Contributions:** Conceptualization, L.D.-L.; Methodology, L.D.-L.; Software, L.D.-L. and J.P.D.-M.; Validation, L.D.-L., J.P.D.-M. and J.C.-J.; Formal analysis, L.D.-L. and J.P.D.-M.; Investigation, L.D.-L.; Resources, L.D.-L., J.P.D.-M., S.V.-D. and A.L.-B.; Data curation, L.D.-L.; Writing--original draft preparation, L.D.-L. and J.P.D.-M.; Writing--review and editing, L.D.-L., J.P.D.-M., [PERSON]-J., [PERSON]-D. and A.L.-B.; Visualization, L.D.-L.; Supervision, J.P.D.-M., [PERSON]-J., [PERSON]-D. and A.L.-B.; project Administration, S.V.-D. and A.L.-B.; Funding acquisition, S.V.-D. and A.L.-B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was partially supported by the Spanish grant (with support from the European Regional Development Fund) COFNET (TEC2016-77785-P), and by the Andalusian Regional Project PAIDI2020 (with FEDER support) PROMETEO (AT17_5410_USE).  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.