# Does Two-Class Training Extract Real Features? A COVID-19 Case Study
## 4 Discussion
### Answering the Questions
* H-C model--Case 1: Mixing the images of pneumonia with those of healthy patients causes the COVID-19 class to be classified quite acceptably (around 92%), presumably due to the range of the maximum concentration of values (_Ch5_). However, for the mixed class, the results are very bad since several important characteristics have very different values between the images of pneumonia and those of healthy patients, such as _Ch1_ (which is similar between pneumonia and COVID-19) and _Ch2_ (which is similar between healthy and COVID-19). * H-C model--Case 2: If we mix the pneumonia images with those of COVID-19, the classification of healthy patients reaches 100% due to the concentration of whites (_Ch4_) and the absence of the peak of blacks (_Ch1_), which clearly distinguishes the histograms. However, regarding the precision when classifying the mixed class, the percentage is significantly reduced due to the differences between the images with COVID-19 and pneumonia, presumably produced by the maximum concentration range of values (_Ch5_). * P-C model--Case 1: If the images of healthy patients are mixed with those of COVID-19, we obtain a high percentage of classification for patients without pneumonia. In this case, the only characteristic that could justify these results is _Ch2_, although for _Ch1_, the black peak is much more significant than that which can be observed for the COVID-19 class. On the other hand, the pneumonia classification obtained very bad results (less than 42%) because, as has been verified, the pneumonia class shares many values of the characteristics extracted with the other classes. * P-C model--Case 2: Finally, combining the images of healthy patients with those of pneumonia, a high classification result can be observed for the mixed class since the pneumonia images share the _Ch4_ and _Ch5_ characteristics with the images of healthy patients, with the _Ch5_ characteristics being the most relevant due to the values' concentration. On the other hand, the results for the classification of the COVID19 class are acceptable (but not excellent), with more than 15% failures due to the similarities in the Ch1 and _Ch3_ characteristics with the pneumonia images. After this qualitative analysis, we can affirm that the theory initially indicated about the use of the gray level concentration values to extract characteristics is feasible. Although we cannot certify with total certainty that the characteristics indicated are all used and that the explanations provided are complete, the intention of this study was not to make a classification based on a decision tree, but to make the reader doubt about the correct feature extraction performed by convolutional DL binary image classification systems. Due to all the presented results and the previous explanations, we can certify that the feature extraction carried out by a binary DL convolutional system is not related to the characteristics that are usually identified by the medical specialist, but rather has its origin in the numerical characteristics of the images. Although there are cases in which the classification of a disease is a binary task (for example, whether or not the patient has cancer), there are other cases (such as the one we present in this work) where the possible outcomes are not binary. Especially when looking at disease symptoms (such as damage to lungs), it is important to understand that there are multiple diseases that can cause them, and therefore, working with a binary classification system may not be realistic or correct. For the results to be useful for specialists, the trained systems have to be as close as possible to reality, distinguishing between all the possible classes that the classification problem may have. This would be the only way to make sure that the trained system is useful.