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SARS-CoV-2 CT-scan dataset: A large dataset of real patients CT scans for SARS-CoV-2 identification

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@article{5f4c9ed367c54d89b04e355bb362b4ba,
title = "SARS-CoV-2 CT-scan dataset: A large dataset of real patients CT scans for SARS-CoV-2 identification",
abstract = "The COVID-19 disease has widely spread all over the world since the beginning of 2020. On January 30, 2020 the World Health Organization (WHO) declared a global health emergency. At the time of writing this paper the number of infected about 2 million people worldwide and took over 125,000 lives, the advanced public health systems of European countries as well as of USA were overwhelmed.In this paper, we propose an eXplainable Deep Learning approach to detect COVID-19 from computer tomography (CT) - Scan images. The rapid detection of any COVID-19 case is of supreme importance to ensure timely treatment. From a public health perspective, rapid patient isolation is also extremely important to curtail the rapid spread of the disease. From this point of view the proposed method offers an easy to use and understand tool to the front-line medics. It is of huge importance not only the statistical accuracy and other measures, but also the ability to understand and interpret how the decision was made. The results demonstrate that the proposed approach is able to surpass the other published results which were using standard Deep Neural Network in terms of performance.Moreover, it produce highly interpretable results which may be helpful for the early detection of the disease by specialists.",
author = "Plamen Angelov and {Almeida Soares}, Eduardo",
year = "2020",
month = apr,
day = "29",
doi = "10.1101/2020.04.24.20078584",
language = "English",
journal = "medRxiv",
publisher = "Cold Spring Harbor Laboratory Press",

}

RIS

TY - JOUR

T1 - SARS-CoV-2 CT-scan dataset

T2 - A large dataset of real patients CT scans for SARS-CoV-2 identification

AU - Angelov, Plamen

AU - Almeida Soares, Eduardo

PY - 2020/4/29

Y1 - 2020/4/29

N2 - The COVID-19 disease has widely spread all over the world since the beginning of 2020. On January 30, 2020 the World Health Organization (WHO) declared a global health emergency. At the time of writing this paper the number of infected about 2 million people worldwide and took over 125,000 lives, the advanced public health systems of European countries as well as of USA were overwhelmed.In this paper, we propose an eXplainable Deep Learning approach to detect COVID-19 from computer tomography (CT) - Scan images. The rapid detection of any COVID-19 case is of supreme importance to ensure timely treatment. From a public health perspective, rapid patient isolation is also extremely important to curtail the rapid spread of the disease. From this point of view the proposed method offers an easy to use and understand tool to the front-line medics. It is of huge importance not only the statistical accuracy and other measures, but also the ability to understand and interpret how the decision was made. The results demonstrate that the proposed approach is able to surpass the other published results which were using standard Deep Neural Network in terms of performance.Moreover, it produce highly interpretable results which may be helpful for the early detection of the disease by specialists.

AB - The COVID-19 disease has widely spread all over the world since the beginning of 2020. On January 30, 2020 the World Health Organization (WHO) declared a global health emergency. At the time of writing this paper the number of infected about 2 million people worldwide and took over 125,000 lives, the advanced public health systems of European countries as well as of USA were overwhelmed.In this paper, we propose an eXplainable Deep Learning approach to detect COVID-19 from computer tomography (CT) - Scan images. The rapid detection of any COVID-19 case is of supreme importance to ensure timely treatment. From a public health perspective, rapid patient isolation is also extremely important to curtail the rapid spread of the disease. From this point of view the proposed method offers an easy to use and understand tool to the front-line medics. It is of huge importance not only the statistical accuracy and other measures, but also the ability to understand and interpret how the decision was made. The results demonstrate that the proposed approach is able to surpass the other published results which were using standard Deep Neural Network in terms of performance.Moreover, it produce highly interpretable results which may be helpful for the early detection of the disease by specialists.

U2 - 10.1101/2020.04.24.20078584

DO - 10.1101/2020.04.24.20078584

M3 - Journal article

JO - medRxiv

JF - medRxiv

ER -