Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Classification of COVID-19 from CXR Images in a 15-class Scenario: an
  Attempt to Avoid Bias in the System   As of June 2021, the World Health Organization (WHO) has reported 171.7
million confirmed cases including 3,698,621 deaths from COVID-19. Detecting
COVID-19 and other lung diseases from Chest X-Ray (CXR) images can be very
effective for emergency diagnosis and treatment as CXR is fast and cheap. The
objective of this study is to develop a system capable of detecting COVID-19
along with 14 other lung diseases from CXRs in a fair and unbiased manner. The
proposed system consists of a CXR image selection technique and a deep learning
based model to classify 15 diseases including COVID-19. The proposed CXR
selection technique aims to retain the maximum variation uniformly and
eliminate poor quality CXRs with the goal of reducing the training dataset size
without compromising classifier accuracy. More importantly, it reduces the
often hidden bias and unfairness in decision making. The proposed solution
exhibits a promising COVID-19 detection scheme in a more realistic situation
than most existing studies as it deals with 15 lung diseases together. We hope
the proposed method will have wider adoption in medical image classification
and other related fields.

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The categories of this paper are: cond-mat
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The categories of this paper are: cmp-lg
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The categories of this paper are: gr-qc
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The categories of this paper are: q-bio
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The categories of this paper are: math-ph
12
The categories of this paper are: nucl-th
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The categories of this paper are: chao-dyn
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The categories of this paper are: stat
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The categories of this paper are: math
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The categories of this paper are: quant-ph
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The categories of this paper are: q-fin
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The categories of this paper are: plasm-ph
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The categories of this paper are: funct-an
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The categories of this paper are: atom-ph
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The categories of this paper are: econ
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The categories of this paper are: dg-ga