Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Deep-Learning Framework for Improving COVID-19 CT Image Quality and
  Diagnostic Accuracy   We present a deep-learning based computing framework for fast-and-accurate CT
(DL-FACT) testing of COVID-19. Our CT-based DL framework was developed to
improve the testing speed and accuracy of COVID-19 (plus its variants) via a
DL-based approach for CT image enhancement and classification. The image
enhancement network is adapted from DDnet, short for DenseNet and Deconvolution
based network. To demonstrate its speed and accuracy, we evaluated DL-FACT
across several sources of COVID-19 CT images. Our results show that DL-FACT can
significantly shorten the turnaround time from days to minutes and improve the
COVID-19 testing accuracy up to 91%. DL-FACT could be used as a software tool
for medical professionals in diagnosing and monitoring COVID-19.

18
The categories of this paper are: econ
33
The categories of this paper are: plasm-ph
15
The categories of this paper are: nucl-ex
13
The categories of this paper are: quant-ph
9
The categories of this paper are: nlin
21
The categories of this paper are: adap-org
6
The categories of this paper are: astro-ph
26
The categories of this paper are: bayes-an
20
The categories of this paper are: acc-phys
24
The categories of this paper are: dg-ga
28
The categories of this paper are: alg-geom
16
The categories of this paper are: stat
37
The categories of this paper are: supr-con
23
The categories of this paper are: solv-int
35
The categories of this paper are: mtrl-th
8
The categories of this paper are: hep-ex