The contamination by SARS-CoV-2 which causes the COVID-19 disease has generally spread everywhere throughout the world since the start of 2020. On January 30, 2020, the World Health Organization (WHO) proclaimed a worldwide health crisis. Analysts of various orders work alongside general health authorities to comprehend the SARS-CoV-2 pathogenesis and together with the policymakers direly create techniques to control the spread of this new disease.

Recent findings have observed imaging patterns on computed tomography (CT) for patients infected by SARS-CoV-2..

In this research, we have used a public available SARS-COV-2 Ct-Scan Dataset, containing 1252 CT scans that are positive for SARS-CoV-2 infection (COVID-19) and 1230 CT scans for patients non-infected by SARS-CoV-2. This dataset of CT scans for SARS-CoV-2 (COVID-19) identification is created by our collaborators, Plamenlancaster: Professor Plamen Angelov from Lancaster University/ Centre Director @ Lira, & his researcher, Eduardo Soares PhD.

These data have been collected from real patients in hospitals from Sao Paulo, Brazil..

The aim of this dataset is to encourage the research and development of artificial intelligent methods which are able to identify if a person is is infected by SARS-CoV-2 through the analysis of his/her CT scans. As baseline result for this dataset we used an eXplainable Deep Learning approach (xDNN) which we could achieve an F1 score of 0.9678 which is very promising..