Abstract
Background: Coronavirus (COVID-19) has appeared first time in Wuhan, China, as an
acute respiratory syndrome and spread rapidly. It has been declared a pandemic by the WHO. Thus,
there is an urgent need to develop an accurate computer-aided method to assist clinicians in identifying
COVID-19-infected patients by computed tomography CT images. The contribution of this paper
is that it proposes a pre-processing technique that increases the recognition rate compared to the
techniques existing in the literature.
Methods: The proposed pre-processing technique, which consists of both contrast enhancement and
open-morphology filter, is highly effective in decreasing the diagnosis error rate. After carrying out
pre-processing, CT images are fed to a 15-layer convolution neural network (CNN) as deep-learning
for the training and testing operations. The dataset used in this research has been publically published,
in which CT images were collected from hospitals in Sao Paulo, Brazil. This dataset is composed
of 2482 CT scans images, which include 1252 CT scans of SARS-CoV-2 infected patients
and 1230 CT scans of non-infected SARS-CoV-2 patients.
Results: The proposed detection method achieves up to 97.8% accuracy, which outperforms the reported
accuracy of the dataset by 97.3%.
Conclusion: The performance in terms of accuracy has been improved up to 0.5% by the proposed
methodology over the published dataset and its method.
Keywords:
COVID-19, CNN, open-morphology filter, deep-learning, pattern recognition, image processing.
Graphical Abstract
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,
Mohammad J.M. Zedan
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