A deep learning model to screen for Corona Virus Disease (COVID-19) from X-ray chest images

Abstract

The overwhelming situation in hospitals, both in countries considered as developed and in under-developed countries alike, because of the pandemic caused by COVID-19, has saturated some national health systems, flooded with long lines of people waiting to get admitted to hospitals, or waiting to get tested for the virus, or waiting for test results, as well as people dying at homes and streets. Although, RT-PCR is an effective technique for COVID-19 diagnosis, it requires expensive scarce equipment, reagents and specialized technicians. Chest radiological imaging, like X-ray and AI approaches can be useful to overcome some of the limitations. A deep learning model for the automatic diagnosis of COVID-19 is proposed in this research, based on a CNN with 3 incremental convolutional blocks and a full connected MLP, which requires raw chest X-ray images to return the probability of identifying the pneumonia caused by this virus, with a 98.2% of accuracy. This work can be useful for those remote places where access to test kits is limited, or for those people who are not able to pay for the cost of the test, or as a support tool for rapid testing to health service providers.