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.

The SARS-CoV-2 xDNN Classifier by Nitin Mane is an open-source implementation of an xPlainable Deep Neural Network (xDNN) classifier. Inspired by SARS-CoV-2 CT-scan dataset: A large dataset of real patients CT scans for SARS-CoV-2 identification by Eduardo Soares, Plamen Angelov, Sarah Biaso, Michele Higa Froes, Daniel Kanda Abe.

This project shows how eXplainable Deep Neural Networks can be used on the HIAS network to provide real-time SARS-CoV-2 classifications to HIAS applications and devices.