Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: POCOVID-Net: Automatic Detection of COVID-19 From a New Lung Ultrasound
  Imaging Dataset (POCUS)   With the rapid development of COVID-19 into a global pandemic, there is an
ever more urgent need for cheap, fast and reliable tools that can assist
physicians in diagnosing COVID-19. Medical imaging such as CT can take a key
role in complementing conventional diagnostic tools from molecular biology,
and, using deep learning techniques, several automatic systems were
demonstrated promising performances using CT or X-ray data. Here, we advocate a
more prominent role of point-of-care ultrasound imaging to guide COVID-19
detection. Ultrasound is non-invasive and ubiquitous in medical facilities
around the globe. Our contribution is threefold. First, we gather a lung
ultrasound (POCUS) dataset consisting of 1103 images (654 COVID-19, 277
bacterial pneumonia and 172 healthy controls), sampled from 64 videos. This
dataset was assembled from various online sources, processed specifically for
deep learning models and is intended to serve as a starting point for an
open-access initiative. Second, we train a deep convolutional neural network
(POCOVID-Net) on this 3-class dataset and achieve an accuracy of 89% and, by a
majority vote, a video accuracy of 92% . For detecting COVID-19 in particular,
the model performs with a sensitivity of 0.96, a specificity of 0.79 and
F1-score of 0.92 in a 5-fold cross validation. Third, we provide an open-access
web service (POCOVIDScreen) that is available at: https://pocovidscreen.org.
The website deploys the predictive model, allowing to perform predictions on
ultrasound lung images. In addition, it grants medical staff the option to
(bulk) upload their own screenings in order to contribute to the growing public
database of pathological lung ultrasound images.
  Dataset and code are available from:
https://github.com/jannisborn/covid19_pocus_ultrasound.
  NOTE: This preprint is superseded by our paper in Applied Sciences:
https://doi.org/10.3390/app11020672

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The categories of this paper are: funct-an
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The categories of this paper are: math-ph
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The categories of this paper are: math
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The categories of this paper are: adap-org
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The categories of this paper are: hep-lat
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The categories of this paper are: q-alg
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The categories of this paper are: hep-ph
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The categories of this paper are: econ
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The categories of this paper are: q-bio
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The categories of this paper are: solv-int
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The categories of this paper are: acc-phys
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The categories of this paper are: nucl-th
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The categories of this paper are: plasm-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: alg-geom
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The categories of this paper are: chem-ph