A large multiclass dataset of CT scans for COVID-19 identification

Almeida Soares, Eduardo and Angelov, Plamen and Biaso, Sarah and Froes, Michele Higa and Abe, D. K. (2024) A large multiclass dataset of CT scans for COVID-19 identification. Evolving Systems, 15 (1). 635–640. ISSN 1868-6478

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Abstract

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.