Vol.40, No.1, 2022, pp.375-388, doi:10.32604/csse.2022.016949
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ARTICLE
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:
Received 16 January 2021; Accepted 28 April 2021; Issue published 26 August 2021
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.
Keywords
X-ray image classification; X-ray feature extraction; COVID-19; coronavirus disease; convolutional neural networks; optimized model
Cite This Article
A. Basalamah and S. Rahman, "An optimized cnn model architecture for detecting coronavirus (covid-19) with x-ray images," Computer Systems Science and Engineering, vol. 40, no.1, pp. 375–388, 2022.
Citations
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