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DOI: 10.14569/IJACSA.2021.01206102
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A Comparative Study of Stand-Alone and Hybrid CNN Models for COVID-19 Detection

Author 1: Wedad Alawad
Author 2: Banan Alburaidi
Author 3: Asma Alzahrani
Author 4: Fai Alflaj

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 6, 2021.

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Abstract: The COVID-19 pandemic continues to impact both the international economy and individual lives. A fast and accurate diagnosis of COVID-19 is required to limit the spread of this disease and reduce the number of infections and deaths. However, a time consuming biological test, Real-Time Reverse Transcription–Polymerase Chain Reaction (RT-PCR), is used to diagnose COVID-19. Furthermore, sometimes the test produces ambiguous results, especially when samples are taken in the early stages of the disease. As a potential solution, machine learning algorithms could help enhance the process of detecting COVID-19 cases. In this paper, we have provided a study that compares the stand-alone CNN model and hybrid machine learning models in their ability to detect COVID-19 from chest X-Ray images. We presented four models to classify such kinds of images into COVID-19 and normal. Visual Geometry Group (VGG-16) is the architecture used to develop the stand-alone CNN model. This hybrid model consists of two parts: the VGG-16 as a features extractor, and a conventional machine learning algorithm, such as support-vector-machines (SVM), Random-Forests (RF), and Extreme-Gradient-Boosting (XGBoost), as a classifier. Even though several studies have investigated this topic, the dataset used in this study is considered one of the largest because we have combined five existing datasets. The results illustrate that there is no noticeable improvement in the performance when hybrid models are used as an alternative to the stand-alone CNN model. VGG-16 and (VGG16+SVM) models provide the best performance with a 99.82% model accuracy and 100% model sensitivity. In general, all the four presented models are reliable, and the lowest accuracy obtained among them is 98.73%.

Keywords: COVID-19; convolutional neural network; hybrid models; chest X-Ray; deep learning

Wedad Alawad, Banan Alburaidi, Asma Alzahrani and Fai Alflaj, “A Comparative Study of Stand-Alone and Hybrid CNN Models for COVID-19 Detection” International Journal of Advanced Computer Science and Applications(IJACSA), 12(6), 2021. http://dx.doi.org/10.14569/IJACSA.2021.01206102

@article{Alawad2021,
title = {A Comparative Study of Stand-Alone and Hybrid CNN Models for COVID-19 Detection},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.01206102},
url = {http://dx.doi.org/10.14569/IJACSA.2021.01206102},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {6},
author = {Wedad Alawad and Banan Alburaidi and Asma Alzahrani and Fai Alflaj}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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