# Does Two-Class Training Extract Real Features? A COVID-19 Case Study
###### Abstract  
Diagnosis aid systems that use image analysis are currently very useful due to the large workload of health professionals involved in making diagnoses. In recent years, Convolutional Neural Networks (CNNs) have been used to help in these tasks. For this reason, multiple studies that analyze the detection precision for several diseases have been developed. However, many of these works distinguish between only two classes: healthy and with a specific disease. Based on this premise, in this work, we try to answer the questions: When training an image classification system with only two classes (healthy and sick), does this system extract the specific features of this disease, or does it only obtain the features that differentiate it from a healthy patient? Trying to answer these questions, we analyze the particular case of COVID-19 detection. Many works that classify this disease using X-ray images have been published; some of them use two classes (with and without COVID-19), while others include more classes (pneumonia, SARS, influenza, etc.). In this work, we carry out several classification studies with two classes, using test images that do not belong to those classes, in order to try to answer the previous questions. The first studies indicate problems in these two-class systems when using a third class as a test, being classified inconsistently. Deeper studies show that deep learning systems trained with two classes do not correctly extract the characteristics of pathologies, but rather differentiate the classes based on the physical characteristics of the images. After the discussion, we conclude that these two-class trained deep learning systems are not valid if there are other diseases that cause similar symptoms. COVID-19; pandemic; deep learning; neural networks; X-ray; medical images +
Footnote †: journal: Medical  
Thus, if the system trained by pulmonary X-ray images classifies between healthy patients and patients with a disease that leads to pneumonia, it is logical to think that the system would focus on detecting lung problems. Thus, it would not be correct to say that the developed system provides an accurate result for that particular disease. In the particular case that concerns us, how is COVID-19 pneumonia different from regular pneumonia? The symptoms of COVID-19 pneumonia can be similar to other types of viral pneumonia. Because of this, it can be difficult to know what is causing this condition without getting tested for COVID-19 or other respiratory infections. Research is underway to determine how COVID-19 pneumonia differs from other types of pneumonia. Information from these studies can potentially aid in the diagnosis and in expanding our understanding of how SARS-CoV-2 affects lungs [4]. That study used Computerized Tomography (CT) scans and laboratory tests to compare the clinical features of COVID-19 pneumonia with other types of pneumonia. The researchers found that people with COVID-19 pneumonia were more likely to have:  
* Pneumonia that affects both lungs instead of just one (bilateral pneumonia). * Lungs that had a characteristic \"ground glass\" appearance on CT scans. * Abnormalities in some laboratory tests, particularly those that were evaluated. However, we are talking about probabilities, not exact rules. Thus, using lung X-ray images, a patient with COVID-19 can be erroneously classified as a patient with any other type of disease that causes similar damages to lungs. Considering the above-mentioned diagnosis aid systems, the availability of multiple Chest X-Ray (CXR) datasets and the advances in the field of deep learning have facilitated the development of techniques for automatic image interpretation [5]. These techniques have been recently applied to COVID-19 detection in CXR images, e.g., [6; 7; 8; 9; 10]. In general, these papers used widely different datasets with different domains and distributions. As an example, Reference [6] used 184 COVID-19 images and 5000 non-COVID-19 images coming from 13 different categories.