The current COVID-19 pandemic, that has caused more than 100 million cases as well as more than two million deaths worldwide, demands the development of fast and accurate diagnostic methods despite the lack of available samples. This disease mainly affects the respiratory system of the patients and can lead to pneumonia and to severe cases of acute respiratory syndrome that result in the formation of several pathological structures in the lungs. These pathological structures can be explored taking advantage of chest X-ray imaging. As a recommendation for the health services, portable chest X-ray devices should be used instead of conventional fixed machinery, in order to prevent the spread of the pathogen. However, portable devices present several problems (specially those related with capture quality). Moreover, the subjectivity and the fatigue of the clinicians lead to a very difficult diagnostic process. To overcome that, computer-aided methodologies can be very useful even taking into account the lack of available samples that the COVID-19 affectation shows. In this work, we propose an improvement in the performance of COVID-19 screening, taking advantage of several cycle generative adversarial networks to generate useful and relevant synthetic images to solve the lack of COVID-19 samples, in the context of poor quality and low detail datasets obtained from portable devices. For validating this proposal for improved COVID-19 screening, several experiments were conducted. The results demonstrate that this data augmentation strategy improves the performance of a previous COVID-19 screening proposal, achieving an accuracy of 98.61% when distinguishing among NON-COVID-19 ( i.e. normal control samples and samples with pathologies others than COVID-19) and genuine COVID-19 samples. It is remarkable that this methodology can be extrapolated to other pulmonary pathologies and even other medical imaging domains to overcome the data scarcity.

Coronavirus Disease 2019 , declared a global pandemic by the World Health Organization, mainly affects the pulmonary tissues, playing chest X-ray images an important role for its screening and early detection. In this context, portable X-ray devices are widely used, representing an alternative to fixed devices in order to reduce risks of crosscontamination. However, they provide lower quality and detailed images in terms of spatial resolution and contrast. In this work, given the low availability of images of this recent disease, we present new approaches to artificially increase the dimensionality of portable chest X-ray datasets for COVID-19 diagnosis. Hence, we combined 3 complementary Cycle-GAN architectures to perform a simultaneous oversampling using an unsupervised strategy and without the necessity of paired data. Despite the poor quality of the portable X-ray images, we provide an overall accuracy of 92.50% in a COVID-19 screening context, proving their suitability for COVID-19 diagnostic tasks.

The global pandemic of COVID-19 raises the importance of having fast and reliable methods to perform an early detection and to visualize the evolution of the disease in every patient, which can be assessed with chest X-ray imaging. Moreover, in order to reduce the risk of cross contamination, radiologists are asked to prioritize the use of portable chest X-ray devices that provide a lower quality and lower level of detail in comparison with the fixed machinery. In this context, computer-aided diagnosis systems are very useful. During the last years, for the case of medical imaging, they are widely developed using deep learning strategies. However, there is a lack of sufficient representative datasets of the COVID-19 affectation, which are critical for supervised learning when training deep models. In this work, we propose a fully automatic method to artificially increase the size of an original portable chest X-ray imaging dataset that was specifically designed for the COVID-19 diagnosis, which can be developed in a non-supervised manner and without requiring paired data. The results demonstrate that the method is able to perform a reliable screening despite all the problems associated with images provided by portable devices, providing an overall accuracy of 92.50%.