Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Residual Network based Deep Learning Model for Detection of COVID-19
  from Cough Sounds   The present work proposes a deep-learning-based approach for the
classification of COVID-19 coughs from non-COVID-19 coughs and that can be used
as a low-resource-based tool for early detection of the onset of such
respiratory diseases. The proposed system uses the ResNet-50 architecture, a
popularly known Convolutional Neural Network (CNN) for image recognition tasks,
fed with the log-Mel spectrums of the audio data to discriminate between the
two types of coughs. For the training and validation of the proposed deep
learning model, this work utilizes the Track-1 dataset provided by the DiCOVA
Challenge 2021 organizers. Additionally, to increase the number of
COVID-positive samples and to enhance variability in the training data, it has
also utilized a large open-source database of COVID-19 coughs collected by the
EPFL CoughVid team. Our developed model has achieved an average validation AUC
of 98.88%. Also, applying this model on the Blind Test Set released by the
DiCOVA Challenge, the system has achieved a Test AUC of 75.91%, Test
Specificity of 62.50%, and Test Sensitivity of 80.49%. Consequently, this
submission has secured 16th position in the DiCOVA Challenge 2021 leader-board.

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The categories of this paper are: supr-con
24
The categories of this paper are: dg-ga
21
The categories of this paper are: adap-org
20
The categories of this paper are: acc-phys
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The categories of this paper are: cmp-lg
35
The categories of this paper are: mtrl-th
10
The categories of this paper are: q-bio
16
The categories of this paper are: stat
6
The categories of this paper are: astro-ph
28
The categories of this paper are: alg-geom
2
The categories of this paper are: math
7
The categories of this paper are: hep-th
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The categories of this paper are: math-ph
3
The categories of this paper are: physics
5
The categories of this paper are: gr-qc
8
The categories of this paper are: hep-ex