The infection by SARS-CoV-2 which causes the COVID-19 disease has widely spread all over the world since the beginning of 2020. On January 30, 2020 the World Health Organization (WHO) declared a global health emergency.Researchers of different disciplines work along with public health officials to understand the SARS-CoV-2 pathogenesis and jointly with the policymakers urgently develop strategies 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 paper, we build 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, 2482 CT scans in total. 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 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 97.31% which is very promising. The proposed dataset is available www.kaggle.com/plameneduardo/sarscov2-ctscan-dataset and xDNN code is available at https://github.com/Plamen-Eduardo/xDNN-SARS-CoV-2-CT-Scan.

The infection by SARS-CoV-2 which causes the COVID-19 disease has spread widely over the whole world since the beginning of 2020. Following the epidemic which started in Wuhan, China on January 30, 2020 the World Health Organization (WHO) declared a global health emergency and a pandemic. In this paper, we describe a publicly available multiclass CT scan dataset for SARS-CoV-2 infection identification. Which currently contains 4173 CT-scans of 210 different patients, out of which 2168 correspond to 80 patients infected with SARS-CoV-2 and confirmed by RT-PCR. These data have been collected in the Public Hospital of the Government Employees of Sao Paulo and the Metropolitan Hospital of Lapa, both in Sao Paulo – Brazil. The aim of this data set is to encourage the research and development of artificial intelligent methods that are able to identify SARS-CoV-2 or other diseases through the analysis of CT scans. As a baseline result for this data set, we used the recently introduced eXplainable Deep Learning approach (xDNN), which is a transparent deep learning approach that allows users to inspect the decisions of the network.