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Developing a Recognition System for Classifying COVID-19 Using a Convolutional Neural Network Algorithm

Fawaz Waselallah Alsaade1, Theyazn H. H. Aldhyani2,*, Mosleh Hmoud Al-Adhaileh3

1 College of Computer Science and Information Technology, King Faisal University, Al-Ahsa, Saudi Arabia
2 Community College of Abqaiq, King Faisal University, Al-Ahsa, Saudi
3 Deanship of E-Learning and Distance Education King Faisal University Saudi Arabia, Al-Ahsa, Saudi Arabia

* Corresponding Author: Theyazn H. H. Aldhyani. Email: email

(This article belongs to this Special Issue: Application of Big Data Analytics in the Management of Business)

Computers, Materials & Continua 2021, 68(1), 805-819. https://doi.org/10.32604/cmc.2021.016264

Abstract

The COVID-19 pandemic poses an additional serious public health threat due to little or no pre-existing human immunity, and developing a system to identify COVID-19 in its early stages will save millions of lives. This study applied support vector machine (SVM), k-nearest neighbor (K-NN) and deep learning convolutional neural network (CNN) algorithms to classify and detect COVID-19 using chest X-ray radiographs. To test the proposed system, chest X-ray radiographs and CT images were collected from different standard databases, which contained 95 normal images, 140 COVID-19 images and 10 SARS images. Two scenarios were considered to develop a system for predicting COVID-19. In the first scenario, the Gaussian filter was applied to remove noise from the chest X-ray radiograph images, and then the adaptive region growing technique was used to segment the region of interest from the chest X-ray radiographs. After segmentation, a hybrid feature extraction composed of 2D-DWT and gray level co-occurrence matrix was utilized to extract the features significant for detecting COVID-19. These features were processed using SVM and K-NN. In the second scenario, a CNN transfer model (ResNet 50) was used to detect COVID-19. The system was examined and evaluated through multiclass statistical analysis, and the empirical results of the analysis found significant values of 97.14%, 99.34%, 99.26%, 99.26% and 99.40% for accuracy, specificity, sensitivity, recall and AUC, respectively. Thus, the CNN model showed significant success; it achieved optimal accuracy, effectiveness and robustness for detecting COVID-19.

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F. Waselallah Alsaade, T. H. H. Aldhyani and M. Hmoud Al-Adhaileh, "Developing a recognition system for classifying covid-19 using a convolutional neural network algorithm," Computers, Materials & Continua, vol. 68, no.1, pp. 805–819, 2021.

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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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