Recognition of COVID-19 disease from X-ray images by hybrid model consisting of
2D curvelet transform, chaotic salp swarm algorithm and deep learning technique
#MMPMID32834627
Altan A
; Karasu S
Chaos Solitons Fractals
2020[Nov]; 140
(?): 110071
PMID32834627
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The novel coronavirus disease 2019 (COVID-19), detected in Wuhan City, Hubei
Province, China in late December 2019, is rapidly spreading and affecting all
countries in the world. Real-time reverse transcription-polymerase chain reaction
(RT-PCR) test has been described by the World Health Organization (WHO) as the
standard test method for the diagnosis of the disease. However, considering that
the results of this test are obtained between a few hours and two days, it is
very important to apply another diagnostic method as an alternative to this test.
The fact that RT-PCR test kits are limited in number, the test results are
obtained in a long time, and the high probability of healthcare personnel
becoming infected with the disease during the test, necessitates the use of other
diagnostic methods as an alternative to these test kits. In this study, a hybrid
model consisting of two-dimensional (2D) curvelet transformation, chaotic salp
swarm algorithm (CSSA) and deep learning technique is developed in order to
determine the patient infected with coronavirus pneumonia from X-ray images. In
the proposed model, 2D Curvelet transformation is applied to the images obtained
from the patient's chest X-ray radiographs and a feature matrix is formed using
the obtained coefficients. The coefficients in the feature matrix are optimized
with the help of the CSSA and COVID-19 disease is diagnosed by the
EfficientNet-B0 model, which is one of the deep learning methods. Experimental
results show that the proposed hybrid model can diagnose COVID-19 disease with
high accuracy from chest X-ray images.
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