Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: COVID-19 identification in chest X-ray images on flat and hierarchical
  classification scenarios   The COVID-19 can cause severe pneumonia and is estimated to have a high
impact on the healthcare system. The standard image diagnosis tests for
pneumonia are chest X-ray (CXR) and computed tomography (CT) scan. CXR are
useful in because it is cheaper, faster and more widespread than CT. This study
aims to identify pneumonia caused by COVID-19 from other types and also healthy
lungs using only CXR images. In order to achieve the objectives, we have
proposed a classification schema considering the multi-class and hierarchical
perspectives, since pneumonia can be structured as a hierarchy. Given the
natural data imbalance in this domain, we also proposed the use of resampling
algorithms in order to re-balance the classes distribution. Our classification
schema extract features using some well-known texture descriptors and also
using a pre-trained CNN model. We also explored early and late fusion
techniques in order to leverage the strength of multiple texture descriptors
and base classifiers at once. To evaluate the approach, we composed a database,
named RYDLS-20, containing CXR images of pneumonia caused by different
pathogens as well as CXR images of healthy lungs. The classes distribution
follows a real-world scenario in which some pathogens are more common than
others. The proposed approach achieved a macro-avg F1-Score of 0.65 using a
multi-class approach and a F1-Score of 0.89 for the COVID-19 identification in
the hierarchical classification scenario. As far as we know, we achieved the
best nominal rate obtained for COVID-19 identification in an unbalanced
environment with more than three classes. We must also highlight the novel
proposed hierarchical classification approach for this task, which considers
the types of pneumonia caused by the different pathogens and lead us to the
best COVID-19 recognition rate obtained here.

32
The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
22
The categories of this paper are: chao-dyn
27
The categories of this paper are: comp-gas
18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
4
The categories of this paper are: cond-mat
30
The categories of this paper are: q-alg
6
The categories of this paper are: astro-ph
20
The categories of this paper are: acc-phys
36
The categories of this paper are: cmp-lg
26
The categories of this paper are: bayes-an
13
The categories of this paper are: quant-ph
2
The categories of this paper are: math
15
The categories of this paper are: nucl-ex
3
The categories of this paper are: physics