CovFrameNet: An Enhanced Deep Learning Framework for COVID-19 Detection

Olaide Nathaniel Oyelade; Absalom El-Shamir Ezugwu; Haruna Chiroma

doi:10.1109/ACCESS.2021.3083516

CovFrameNet: An Enhanced Deep Learning Framework for COVID-19 Detection

IEEE Access. 2021 May 25:9:77905-77919. doi: 10.1109/ACCESS.2021.3083516. eCollection 2021.

Authors

Olaide Nathaniel Oyelade^{1

2}, Absalom El-Shamir Ezugwu¹, Haruna Chiroma³

Affiliations

¹ School of Mathematics, Statistics, and Computer ScienceUniversity of KwaZulu-Natal at Pietermaritzburg Pietermaritzburg 3201 South Africa.
² Department of Computer ScienceFaculty of Physical SciencesAhmadu Bello University Zaria 810211 Nigeria.
³ Future Technology Research CenterNational Yunlin University of Science and Technology Douliu 64002 Taiwan.

Abstract

The novel coronavirus, also known as COVID-19, is a pandemic that has weighed heavily on the socio-economic affairs of the world. Research into the production of relevant vaccines is progressively being advanced with the development of the Pfizer and BioNTech, AstraZeneca, Moderna, Sputnik V, Janssen, Sinopharm, Valneva, Novavax and Sanofi Pasteur vaccines. There is, however, a need for a computational intelligence solution approach to mediate the process of facilitating quick detection of the disease. Different computational intelligence methods, which comprise natural language processing, knowledge engineering, and deep learning, have been proposed in the literature to tackle the spread of coronavirus disease. More so, the application of deep learning models have demonstrated an impressive performance compared to other methods. This paper aims to advance the application of deep learning and image pre-processing techniques to characterise and detect novel coronavirus infection. Furthermore, the study proposes a framework named CovFrameNet., which consist of a pipelined image pre-processing method and a deep learning model for feature extraction, classification, and performance measurement. The novelty of this study lies in the design of a CNN architecture that incorporates an enhanced image pre-processing mechanism. The National Institutes of Health (NIH) Chest X-Ray dataset and COVID-19 Radiography database were used to evaluate and validate the effectiveness of the proposed deep learning model. Results obtained revealed that the proposed model achieved an accuracy of 0.1, recall/precision of 0.85, F-measure of 0.9, and specificity of 1.0. Thus, the study's outcome showed that a CNN-based method with image pre-processing capability could be adopted for the pre-screening of suspected COVID-19 cases, and the confirmation of RT-PCR-based detected cases of COVID-19.

Keywords: CNN; COVID-19; Image pre-processing; X-Ray; convolutional neural network; coronavirus; deep learning; machine learning.

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