# Does Two-Class Training Extract Real Features? A COVID-19 Case Study
## 4 Discussion
Therefore, if the world is divided between pneumonia and COVID-19 classes, the healthy images are much more similar to pneumonia than COVID-19. Focusing on the results obtained in Table 5 and according to the previous explanation, the system seems to behave correctly: 96.21% of the healthy images are classified as pneumonia. Therefore, in this case, the model seems to be medically valid. From a medical utility point of view in the case of COVID-19 patient classification, the H-P model does not make much sense. However, the H-C and P-C models do have practical utility: the first model could help to distinguish between healthy and sick patients and the second one to distinguish classic pneumonia from another severe pneumonia caused by COVID-19. Regarding those models, according to the previous explanation, the second one has been tested to be medically useful even if there is no healthy class trained in the system. However, the most useful binary classifier is potentially the H-C model (healthy vs. COVID-19), and this can be observed in the publications presented in Table 2.  
Deeply analyzing the pseudo-confusion matrices presented in the Results Section, we can observe:  
* H-P Model--Case 1 (Figure 3, top-left): As can be observed, many of the non-pneumonia images are classified as pneumonia (more than 46%), so the case of mixing healthy and COVID-19 classes is not medically valid. * H-C Model--Case 2 (Figure 3, top-right): The system presents more than 13% of false positives when classifying healthy patients. Joining the COVID-19 and pneumonia classes produces acceptable results for distinguishing between sick and healthy patients, but the results show almost 8% false negative results for the diseased class. However, as detailed before, the H-P model is not useful to distinguish COVID-19 patients. * P-C Model--Case 1 (Figure 3, middle-left): the integration of the pneumonia class with the healthy class may seem the most logical and easy election due to its use in previous works. For this case, the system obtains more than 31% of false positives for the COVID-19 class, so the results are not acceptable. * H-P Model--Case 2 (Figure 3, middle-right): Using pneumonia images with COVID-19 may not seem very useful for COVID-19 detection, but in this case, there is 100% of healthy detection. However, there are more than 22% false negatives for diseased. * H-C Model--Case 1 (Figure 3, bottom-left): For this case, joining the healthy and COVID-19 classes produces more than 92% of true positives for non-pneumonia, but the pneumonia class obtains almost 60% of false negatives. The results are not acceptable. * P-C Model--Case 2 (Figure 3, bottom-right): Finally, the integration of healthy images with the pneumonia class improves the previous results. However, there is more than 15% of false negatives for the COVID-19 class, which is very dangerous for pandemic spread. Moreover, after analyzing the results of the pseudo confusion matrices, we can conclude that none of the models presented in the Results section satisfies the requirements of the classifier with guarantees. There are only two cases that obtain acceptable results: Case 2 of the H-P model, but this system does not make sense for the detection of COVID-19; and Case 2 of the P-C model. However, for the latter case, the high rate of false negatives when detecting COVID-19 patients is very dangerous. Furthermore, for the model most widely used for binary classifiers in previous works (H-C model), the results are not acceptable at all, as it obtains results below 70% for some classes. Thus, after presenting the analytical results, we want to finish this work going back to the Introduction and trying to answer the questions initially posed: Does the use of two classes in disease classification systems based on convolutional networks correctly extract the characteristics that distinguish that disease, or, on the contrary, does it only extract the characteristics that differentiate a healthy patient from another with the disease?