  The coronavirus outbreak became a major concern for society worldwide.
Technological innovation and ingenuity are essential to fight COVID-19 pandemic
and bring us one step closer to overcome it. Researchers over the world are
working actively to find available alternatives in different fields, such as
the Healthcare System, pharmaceutic, health prevention, among others. With the
rise of artificial intelligence (AI) in the last 10 years, IA-based
applications have become the prevalent solution in different areas because of
its higher capability, being now adopted to help combat against COVID-19. This
work provides a fast detection system of COVID-19 characteristics in X-Ray
images based on deep learning (DL) techniques. This system is available as a
free web deployed service for fast patient classification, alleviating the high
demand for standards method for COVID-19 diagnosis. It is constituted of two
deep learning models, one to differentiate between X-Ray and non-X-Ray images
based on Mobile-Net architecture, and another one to identify chest X-Ray
images with characteristics of COVID-19 based on the DenseNet architecture. For
real-time inference, it is provided a pair of dedicated GPUs, which reduce the
computational time. The whole system can filter out non-chest X-Ray images, and
detect whether the X-Ray presents characteristics of COVID-19, highlighting the
most sensitive regions.
