Please use this identifier to cite or link to this item: https://covid-19.conacyt.mx/jspui/handle/1000/2296
Coronavirus Detection and Analysis on Chest CT with Deep Learning
Ophir Gozes.
Maayan Frid-Adar.
Nimrod Sagie.
Huangqi Zhang.
Wenbin Ji.
Hayit Greenspan.
Acceso Abierto
Atribución-NoComercial-SinDerivadas
https://arxiv.org/pdf/2004.02640v1.pdf
The outbreak of the novel coronavirus, officially declared a global pandemic, has a severe impact on our daily lives. As of this writing there are approximately 197,188 confirmed cases of which 80,881 are in "Mainland China" with 7,949 deaths, a mortality rate of 3.4%. In order to support radiologists in this overwhelming challenge, we develop a deep learning based algorithm that can detect, localize and quantify severity of COVID-19 manifestation from chest CT scans. The algorithm is comprised of a pipeline of image processing algorithms which includes lung segmentation, 2D slice classification and fine grain localization. In order to further understand the manifestations of the disease, we perform unsupervised clustering of abnormal slices. We present our results on a dataset comprised of 110 confirmed COVID-19 patients from Zhejiang province, China.
arxiv.org
2020
Artículo
https://arxiv.org/pdf/2004.02640v1.pdf
Inglés
VIRUS RESPIRATORIOS
Appears in Collections:Artículos científicos

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