Open Access


Classification COVID-19 Based on Enhancement X-Ray Images and Low Complexity Model

Aymen Saad1, Israa S. Kamil2, Ahmed Alsayat3, Ahmed Elaraby4,*
1 Department of Information Technology, Technical College of Management, Kufa, Al Furat Alawsat University, Iraq
2 Department of Medical Laboratory Techniques, Al-Mustaqbal University College, Hilla, Iraq
3 Department of Computer Science, College of Computer and Information Sciences, Jouf University, Saudi Arabia
4 Department of Computer Science, Faculty of Computers and Information, South Valley University, Qena, Egypt
* Corresponding Author: Ahmed Elaraby. Email:
(This article belongs to this Special Issue: Artificial Intelligence and Machine Learning Algorithms in Real-World Applications and Theories)

Computers, Materials & Continua 2022, 72(1), 561-576.

Received 24 September 2021; Accepted 15 December 2021; Issue published 24 February 2022


COVID-19 has been considered one of the recent epidemics that occurred at the last of 2019 and the beginning of 2020 that world widespread. This spread of COVID-19 requires a fast technique for diagnosis to make the appropriate decision for the treatment. X-ray images are one of the most classifiable images that are used widely in diagnosing patients’ data depending on radiographs due to their structures and tissues that could be classified. Convolutional Neural Networks (CNN) is the most accurate classification technique used to diagnose COVID-19 because of the ability to use a different number of convolutional layers and its high classification accuracy. Classification using CNNs techniques requires a large number of images to learn and obtain satisfactory results. In this paper, we used SqueezNet with a modified output layer to classify X-ray images into three groups: COVID-19, normal, and pneumonia. In this study, we propose a deep learning method with enhance the features of X-ray images collected from Kaggle, Figshare to distinguish between COVID-19, Normal, and Pneumonia infection. In this regard, several techniques were used on the selected image samples which are Unsharp filter, Histogram equal, and Complement image to produce another view of the dataset. The Squeeze Net CNN model has been tested in two scenarios using the 13,437 X-ray images that include 4479 for each type (COVID-19, Normal and Pneumonia). In the first scenario, the model has been tested without any enhancement on the datasets. It achieved an accuracy of 91%. But, in the second scenario, the model was tested using the same previous images after being improved by several techniques and the performance was high at approximately 95%. The conclusion of this study is the used model gives higher accuracy results for enhanced images compared with the accuracy results for the original images. A comparison of the outcomes demonstrated the effectiveness of our DL method for classifying COVID-19 based on enhanced X-ray images.


COVID-19; X-ray; image enhancement; classification; CNN; SqueezNet model

Cite This Article

A. Saad, I. S. Kamil, A. Alsayat and A. Elaraby, "Classification covid-19 based on enhancement x-ray images and low complexity model," Computers, Materials & Continua, vol. 72, no.1, pp. 561–576, 2022.

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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