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.
Highlights
The 2019 novel coronavirus disease presents an important and urgent threat to global health
Motivated by the widely reported role of chest X-Rays in enabling the detection of COVID-19 [59], [60], this paper proposes the application of image pre-processing and deep learning techniques to automate the process of extracting important features
In this paper, a deep learning model based on convolution neural network (CNN) was designed and implemented to detect and classify the presence of COVID-19 in chest X-Rays and Computed Tomography (CT) images
Summary
The 2019 novel coronavirus disease presents an important and urgent threat to global health. It has exposed the fragility of the most highly placed health institutions and infrastructures across the globe [1], [2]. Singh et al [4] developed a deep convolution neural network (CNN) that was applied in the automated diagnosis and analysis of COVID-19 in infected patients. Their model involved tuning hyper-parameters of the CNN model with a multi-objective adaptive differential evolution algorithm. The comparative analysis showed that their proposed method outperformed existing machine learning models such as CNN, GA- and PSO-based CNN models, based on the different performance metrics employed to validate the conducted experiment, such as the F-measure and Sensitivity
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