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

Xiaoyan Jiang; Mackenzie Brown; Hei-Ran Cheong; Zuojin Hu

doi:10.4108/eetel.v8i1.2344

Authors

Xiaoyan Jiang Nanjing Normal University of Special Education
Mackenzie Brown Edith Cowan University
Hei-Ran Cheong University of Ulsan
Zuojin Hu Nanjing Normal University of Special Education

DOI:

https://doi.org/10.4108/eetel.v8i1.2344

Keywords:

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

Abstract

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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