COVID-CT-MD, COVID-19 computed tomography scan dataset applicable in machine learning and deep learning

Parnian Afshar; Shahin Heidarian; Nastaran Enshaei; Farnoosh Naderkhani; Moezedin Javad Rafiee; Anastasia Oikonomou; Faranak Babaki Fard; Kaveh Samimi; Konstantinos N. Plataniotis; Arash Mohammadi

doi:10.1038/s41597-021-00900-3

Scientific Data (Apr 2021)

COVID-CT-MD, COVID-19 computed tomography scan dataset applicable in machine learning and deep learning

Parnian Afshar,
Shahin Heidarian,
Nastaran Enshaei,
Farnoosh Naderkhani,
Moezedin Javad Rafiee,
Anastasia Oikonomou,
Faranak Babaki Fard,
Kaveh Samimi,
Konstantinos N. Plataniotis,
Arash Mohammadi

Affiliations

Parnian Afshar: Concordia Institute for Information Systems Engineering (CIISE), Concordia University
Shahin Heidarian: Department of Electrical and Computer Engineering, Concordia University
Nastaran Enshaei: Concordia Institute for Information Systems Engineering (CIISE), Concordia University
Farnoosh Naderkhani: Concordia Institute for Information Systems Engineering (CIISE), Concordia University
Moezedin Javad Rafiee: Department of Medicine and Diagnostic Radiology, McGill University Health Center-Research Institute
Anastasia Oikonomou: Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto
Faranak Babaki Fard: Faculty of Medicine, University of Montreal
Kaveh Samimi: Department of Radiology, Iran university of medical science
Konstantinos N. Plataniotis: Department of Electrical and Computer Engineering, University of Toronto
Arash Mohammadi: Concordia Institute for Information Systems Engineering (CIISE), Concordia University

DOI: https://doi.org/10.1038/s41597-021-00900-3
Journal volume & issue: Vol. 8, no. 1
pp. 1 – 8

Abstract

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Measurement(s) Low Dose Computed Tomography of the Chest • viral infectious disease Technology Type(s) digital curation • image processing technique Factor Type(s) sex • gender • age group • weight • clinical characteristics • covid-19 RT-PCR result • follow-up data Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.13583015

Published in Scientific Data

ISSN: 2052-4463 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/sdata/

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