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

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