# Does Two-Class Training Extract Real Features? A COVID-19 Case Study
## 4 Discussion
### Answering the Questions  
Previously, various DL binary classifiers based on the detection of lung associated pathologies have been tested. For this purpose, they have been tested in extreme conditions where an external class not used in the training phase has been used as a test class. However, this third class is related to the problem that each model has tried to solve; so, eventually in real life, images from this class may be used as inputs in the developed systems (even accidentally). Furthermore, if a new pathology that causes lung injuries appears in the future, it is important to know how the current systems would act. The purpose of this study is to try to understand the feature extraction phase of a convolutional DL system. If the trained system correctly extracts the most relevant features from the images (as a perfect system should do), the main feature would be the lung damage present in each image. If this is the case, when testing a new class, the system should classify these images as the already trained class whose lung damage most closely resemble the new class. According to the results obtained in the previous subsection, we can affirm that the binary systems tested with X-ray images for the detection of lung associated pathologies do not act according to a system that has correctly extracted the relevant characteristics of the images. This affirmation means that, in those cases, these systems would not work correctly with the inclusion of an untrained third class. Therefore, the feature extraction process was not done adequately for those models. Thus, the questions initially presented can be answered thanks to these results:  
* _Does the use of two classes in disease classification systems based on convolutional networks correctly extract the characteristics that distinguish that disease_? No, it does not. According to the previous results obtained after testing several cases and combinations of binary classifiers for lung damage detection, we demonstrate that the behavior of the models trained with images of untrained classes is not correct and does not behave as if the characteristics of the trained classes had been correctly extracted. * _Does it only extract the characteristics that differentiate a healthy patient from another with the disease?_ It seems it does, although we cannot affirm this categorically. The operation of the training process of DL systems and the good classification results initially obtained for each binary model for its training classes allow us to affirm with relative security that the system only focuses on detecting the differences between the classes used during the training process. In this way, the two unknowns that initially arise in this work have been answered. However, according to the obtained results, a new question that we try to answer below appears. If the binary systems studied above do not extract the relevant features from the images used to train them, what features are they extracting during training? Can these be known? Leaving aside temporarily the topic discussed above about lung damage, if we extend this question to a generic DL system that classifies grayscale images, it is logical to think that the distinction between the different classes that it is classifying could be related to the gray levels of the images involved in the training. Thus, we carry out a preliminary study about the gray levels of the images involved in the previously trained models: healthy, pneumonia, and COVID-19. First, we randomly select an image from each class, apply the pre-processing (in the same way as carried out before training), and calculate its histogram. The obtained results are shown in Figure 4.  
At first sight, it is not easy to detect the differences in the X-ray images in some cases. Greater inflammation of the alveoli can be seen in the image with COVID-10, but it is difficult to distinguish it from the inflammation that can be seen in the image with pneumonia.