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ISSN (Print): 1872-2121
ISSN (Online): 2212-4047

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Research Article

COVID-19 Improved Diagnoses Based on the Open-morphology Filter and Deep-learning

Author(s): Majid D. Younus, Mohammad J.M. Zedan, Fahad L. Malallah* and Mustafa G. Saeed

Volume 16, Issue 3, 2022

Published on: 18 June, 2021

Article ID: e180621194172 Pages: 9

DOI: 10.2174/1872212115666210618120249

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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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