Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: COVIDLite: A depth-wise separable deep neural network with white balance
  and CLAHE for detection of COVID-19   Background and Objective:Currently, the whole world is facing a pandemic
disease, novel Coronavirus also known as COVID-19, which spread in more than
200 countries with around 3.3 million active cases and 4.4 lakh deaths
approximately. Due to rapid increase in number of cases and limited supply of
testing kits, availability of alternative diagnostic method is necessary for
containing the spread of COVID-19 cases at an early stage and reducing the
death count. For making available an alternative diagnostic method, we proposed
a deep neural network based diagnostic method which can be easily integrated
with mobile devices for detection of COVID-19 and viral pneumonia using Chest
X-rays (CXR) images. Methods:In this study, we have proposed a method named
COVIDLite, which is a combination of white balance followed by Contrast Limited
Adaptive Histogram Equalization (CLAHE) and depth-wise separable convolutional
neural network (DSCNN). In this method, white balance followed by CLAHE is used
as an image preprocessing step for enhancing the visibility of CXR images and
DSCNN trained using sparse cross entropy is used for image classification with
lesser parameters and significantly lighter in size, i.e., 8.4 MB without
quantization. Results:The proposed COVIDLite method resulted in improved
performance in comparison to vanilla DSCNN with no pre-processing. The proposed
method achieved higher accuracy of 99.58% for binary classification, whereas
96.43% for multiclass classification and out-performed various state-of-the-art
methods. Conclusion:Our proposed method, COVIDLite achieved exceptional results
on various performance metrics. With detailed model interpretations, COVIDLite
can assist radiologists in detecting COVID-19 patients from CXR images and can
reduce the diagnosis time significantly.

21
The categories of this paper are: adap-org
2
The categories of this paper are: math
12
The categories of this paper are: nucl-th
6
The categories of this paper are: astro-ph
18
The categories of this paper are: econ
15
The categories of this paper are: nucl-ex
14
The categories of this paper are: math-ph
7
The categories of this paper are: hep-th
35
The categories of this paper are: mtrl-th
25
The categories of this paper are: patt-sol
4
The categories of this paper are: cond-mat
16
The categories of this paper are: stat
3
The categories of this paper are: physics
8
The categories of this paper are: hep-ex
36
The categories of this paper are: cmp-lg
31
The categories of this paper are: ao-sci