Covid-19 Diagnosis by Gray-level Cooccurrence Matrix and Genetic Algorithm


  • Xiaoyan Jiang Nanjing Normal University of Special Education
  • Mackenzie Brown Edith Cowan University image/svg+xml
  • Zuojin Hu Nanjing Normal University of Special Education
  • Hei-Ran Cheong University of Ulsan image/svg+xml



Gray-level Cooccurrence Matrix, Genetic Algorithm, optimization, Feedforward Neural Network, K-fold cross-validation, COVID-19, Diagnosis


Currently, improving the identification of COVID-19 with the help of computer vision and artificial intelligence has received great attention from researchers. This paper proposes a novel method for automatic detection of COVID-19 based on chest CT to help radiologists improve the speed and reliability of tests for diagnosing COVID-19. Our algorithm is a hybrid approach based on the Gray-level Cooccurrence Matrix and Genetic Algorithm. The Gray-level Cooccurrence Matrix (GLCM) was used to extract CT scan image features, GA algorithm was used as an optimizer, and a feedforward neural network was used as a classifier. Finally, we use 296 chest CT scan images to evaluate the detection performance of our proposed method. To more accurately evaluate the accuracy of the algorithm, 10-run 10-fold cross-validation was introduced. Experimental results show that our proposed method outperforms state-of-the-art methods in terms of Sensitivity, Accuracy, F1, MCC, and FMI.


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How to Cite

X. Jiang, M. Brown, Z. Hu, and H.-R. Cheong, “Covid-19 Diagnosis by Gray-level Cooccurrence Matrix and Genetic Algorithm”, EAI Endorsed Trans e-Learn, vol. 8, no. 1, p. e2, Aug. 2022.