Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: COVID-19 Detection in Computed Tomography Images with 2D and 3D
  Approaches   Detecting COVID-19 in computed tomography (CT) or radiography images has been
proposed as a supplement to the definitive RT-PCR test. We present a deep
learning ensemble for detecting COVID-19 infection, combining slice-based (2D)
and volume-based (3D) approaches. The 2D system detects the infection on each
CT slice independently, combining them to obtain the patient-level decision via
different methods (averaging and long-short term memory networks). The 3D
system takes the whole CT volume to arrive to the patient-level decision in one
step. A new high resolution chest CT scan dataset, called the IST-C dataset, is
also collected in this work. The proposed ensemble, called IST-CovNet, obtains
90.80% accuracy and 0.95 AUC score overall on the IST-C dataset in detecting
COVID-19 among normal controls and other types of lung pathologies; and 93.69%
accuracy and 0.99 AUC score on the publicly available MosMed dataset that
consists of COVID-19 scans and normal controls only. The system is deployed at
Istanbul University Cerrahpasa School of Medicine.

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The categories of this paper are: hep-lat
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The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an
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The categories of this paper are: plasm-ph
32
The categories of this paper are: atom-ph
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The categories of this paper are: q-fin
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The categories of this paper are: math-ph
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The categories of this paper are: solv-int
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The categories of this paper are: nucl-ex
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The categories of this paper are: econ
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The categories of this paper are: mtrl-th
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The categories of this paper are: alg-geom
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The categories of this paper are: q-alg
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The categories of this paper are: comp-gas
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The categories of this paper are: ao-sci
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The categories of this paper are: adap-org