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An Optimized CNN Model Architecture for Detecting Coronavirus (COVID-19) with X-Ray Images

Anas Basalamah1, Shadikur Rahman2,*

1 Umm Al-Qura University, Makkah, Saudi Arabia
2 Daffodil International University, Dhaka, Bangladesh

* Corresponding Author: Shadikur Rahman. Email: email

Computer Systems Science and Engineering 2022, 40(1), 375-388. https://doi.org/10.32604/csse.2022.016949

Abstract

This paper demonstrates empirical research on using convolutional neural networks (CNN) of deep learning techniques to classify X-rays of COVID-19 patients versus normal patients by feature extraction. Feature extraction is one of the most significant phases for classifying medical X-rays radiography that requires inclusive domain knowledge. In this study, CNN architectures such as VGG-16, VGG-19, RestNet50, RestNet18 are compared, and an optimized model for feature extraction in X-ray images from various domains involving several classes is proposed. An X-ray radiography classifier with TensorFlow GPU is created executing CNN architectures and our proposed optimized model for classifying COVID-19 (Negative or Positive). Then, 2,134 X-rays of normal patients and COVID-19 patients generated by an existing open-source online dataset were labeled to train the optimized models. Among those, the optimized model architecture classifier technique achieves higher accuracy (0.97) than four other models, specifically VGG-16, VGG-19, RestNet18, and RestNet50 (0.96, 0.72, 0.91, and 0.93, respectively). Therefore, this study will enable radiologists to more efficiently and effectively classify a patient’s coronavirus disease.

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APA Style
Basalamah, A., Rahman, S. (2022). An optimized CNN model architecture for detecting coronavirus (COVID-19) with x-ray images. Computer Systems Science and Engineering, 40(1), 375-388. https://doi.org/10.32604/csse.2022.016949
Vancouver Style
Basalamah A, Rahman S. An optimized CNN model architecture for detecting coronavirus (COVID-19) with x-ray images. Comput Syst Sci Eng. 2022;40(1):375-388 https://doi.org/10.32604/csse.2022.016949
IEEE Style
A. Basalamah and S. Rahman, "An Optimized CNN Model Architecture for Detecting Coronavirus (COVID-19) with X-Ray Images," Comput. Syst. Sci. Eng., vol. 40, no. 1, pp. 375-388. 2022. https://doi.org/10.32604/csse.2022.016949

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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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