# Deep Learning System for COVID-19 Diagnosis Aid Using X-ray Pulmonary Images
## 1 Introduction
This comparison is performed using the initial gene sequence released by the Shanghai Public Health Clinical Center & School of Public Health (Fudan University, Shanghai, China), and subsequent confirmatory evaluation by additional labs. If the test result is positive, it is recommended that the test is repeated for verification. In patients with confirmed COVID-19 diagnosis, the laboratory evaluation should be repeated to evaluate for viral clearance prior to being released from observation. As detailed above, actual procedures to diagnose COVID-19 patients require several hours to obtain a result. Moreover, those exams may be negative if the patient was infected recently. In other viral diseases that affects breathing, such as influenza or SARS, the damage produced to the lungs can be observed using pulmonary X-ray images. The works presented in Reference [11; 12] detailed these effects in influenza, while other works like Reference [13; 14] explain the effects in SARS patients. So, it is logical to think that this relationship is maintained with COVID-19 patients, since this disease mainly attacks the lungs. However, classic pneumonia patients experience some symptoms similar to those COVID-19 patients in the early stages of the contagion, although with lower virulence. Even so, this fact must be taken into account to correctly diagnose this disease. However, the study of medical images has experienced a great progress with the inclusion of Machine Learning systems capable of automatically extract the necessary characteristics to make a correct diagnosis [15]. Moreover, in the last years these technology has evolved to a concrete branch known as Deep learning. While in Machine Learning the user gives the system a huge amount of rules to solve the problem, in Deep Learning the user gives the system a network model and only a few instructions to modify the model when errors occur. So, using Deep Learning, it is easier and faster to train the classification system. All of them require a dataset made up of several images corresponding to ill patients and healthy patients (all of them previously labeled by a professional). Using this knowledge, neural network-based systems are able to automatically analyze those images and extract the characteristics necessary to diagnose the illness. These systems require several steps like a pre-processing stage, the correct choice of the network architecture, a training stage (that sometimes requires supervision), among others. In Deep Learning systems, although the network model is already established, it is very common to use a pre-processing step to adapt the inputs to the ones needed by that model. These techniques have been used in multiple industrial and medical systems, obtaining very good results [16; 17]. Regarding its application to the medical images analysis, there are several studies that demonstrate that the results obtained are better that the ones obtained by classical diagnostic systems [18]. Furthermore, its application and effectiveness have been proven in other works [19; 20; 21]. So, based on these premises, this work consists of using Machine Learning techniques applied to medical X-ray images of the lung of the patients to obtain an aid system for COVID-19 diagnosis. It is important to emphasize that there are other imaging tools to detect COVID-19 like RM or CT; however, the objective of this work is not to obtain images from patients, but using an existing dataset that meets all the requirements. And, to achieve this purpose, a public dataset that contains X-ray images about healthy, pneumonia and COVID-19 patients all over the world is used. This dataset has mainly X-ray images and this is the justification of choosing X-ray images. With the information included in the dataset, a Deep Learning system is trained and the classification results are detailed in this work.