COVID-CT-MD: COVID-19 Computed Tomography (CT) 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 1 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:arXiv.org 2020-09
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
Format: Article
Language:English
Subjects:
Artificial neural networks
Computed tomography
Coronaviruses
COVID-19
Datasets
Deep learning
Diagnostic systems
Machine learning
Polymerase chain reaction
Radiographs
Sensitivity
Tomography
Viral diseases
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Summary:Novel Coronavirus (COVID-19) has drastically overwhelmed more than 200 countries affecting millions and claiming almost 1 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 subjects 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.
ISSN:2331-8422