Explainable-By-Design Approach For COVID-19 Classification Via CT-Scan

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.