# Does Two-Class Training Extract Real Features? A COVID-19 Case Study
###### Abstract
As a second example, Reference [8] used 358 CXR images from 266 COVID-19 patient cases, 8066 with no pneumonia (i.e., normal) and 5538 with non-COVID-19 pneumonia. As a further example, Reference [7] distinguished only between COVID-19 and tuberculosis. In general, we can find tools that distinguish between several classes (e.g., COVID-19, non-COVID-19 pneumonia, and healthy) or binary systems. As the first step, we need to study the various datasets and works related to this topic. However, due to the extensiveness of these studies, they are included in the next section. Thus, generalizing this problem, it is important to study whether the classification systems based on only two classes (healthy or sick) behave correctly. The main problem with binary systems is that some of them do not try to cover the spectrum of all possible cases that can be found on real CXR and consider only the characteristics that distinguish the two analyzed classes. A priori, and according to current studies, if the disease whose images are used to train the DL system has very characteristic features that distinguish it from any other disease, it is feasible to think about this possibility. However, if the classifier is trained with images that focus on symptoms generated by that disease and that may appear in other cases (not being exclusive to the trained disease), it is fair to think that the system could make errors if it is tested with images of patients with other diseases with similar symptoms not included in the training dataset. Thus, in this work, we ask the question: 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 specific disease? In the rest of the paper, we deal with the real-world problem of diagnosis aids originally trained with a specific domain and distribution and later used in a more realistic situation. To try to answer these questions, in this work, various studies are carried out in which DL systems for lung diseases' classification are trained. In these studies, exclusively two-class training is carried out, and images from another untrained class is used to test the system. Of course, diseases that share symptoms of pneumonia are used in order to observe the behavior of the trained systems. In addition, considering the current global situation and focusing this study on a topic that is now very relevant, in this work, we focus on the application case of COVID-19 detection. In this way, we consider three main classes: healthy, pneumonia, and COVID-19. For this study, we train DL two-class classification systems that distinguish between:  
* Healthy patients and COVID-19 patients; using pneumonia patients as the test case. * Healthy patients and patients with pneumonia; using COVID-19 patients as the test case. * Patients with pneumonia and patients with COVID; using healthy patients as the test case. The rest of the paper is divided as follows: First, in the Materials and Methods Section, the different datasets and published works are detailed, as well as the analysis methodology used to test the different systems. Next, the results obtained after training and testing the different systems are detailed and explained in the Results and Discussion Sections. Finally, conclusions are presented.