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

Novel Coronavirus (COVID-19) has drastically overwhelmed more than 200 countries affecting millions and claiming almost 2 million lives, since its emergence in late 2019. This highly contagious disease can easily spread, and if not controlled in a timely fashion, can rapidly incapacitate healthcare...

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Bibliographic Details
Published in:Scientific data 2021-04, Vol.8 (1), p.121-121, Article 121
Main Authors: Afshar, Parnian, Heidarian, Shahin, Enshaei, Nastaran, Naderkhani, Farnoosh, Rafiee, Moezedin Javad, Oikonomou, Anastasia, Fard, Faranak Babaki, Samimi, Kaveh, Plataniotis, Konstantinos N., Mohammadi, Arash
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Language:English
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Summary:Novel Coronavirus (COVID-19) has drastically overwhelmed more than 200 countries affecting millions and claiming almost 2 million lives, since its emergence in late 2019. This highly contagious disease can easily spread, and if not controlled in a timely fashion, can rapidly incapacitate healthcare systems. The current standard diagnosis method, the Reverse Transcription Polymerase Chain Reaction (RT- PCR), is time consuming, and subject to low sensitivity. Chest Radiograph (CXR), the first imaging modality to be used, is readily available and gives immediate results. However, it has notoriously lower sensitivity than Computed Tomography (CT), which can be used efficiently to complement other diagnostic methods. This paper introduces a new COVID-19 CT scan dataset, referred to as COVID-CT-MD, consisting of not only COVID-19 cases, but also healthy and participants infected by Community Acquired Pneumonia (CAP). COVID-CT-MD dataset, which is accompanied with lobe-level, slice-level and patient-level labels, has the potential to facilitate the COVID-19 research, in particular COVID-CT-MD can assist in development of advanced Machine Learning (ML) and Deep Neural Network (DNN) based solutions. 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
ISSN:2052-4463
2052-4463
DOI:10.1038/s41597-021-00900-3