COVID-19 Diagnosis by Multiple-Distance Gray-Level Cooccurrence Matrix and Genetic Algorithm
Author:
Affiliation:
1. School of Mathematics Information Science, Nanjing Normal University of Special Education, China
2. School of Engineering, Edith Cowan University, Australia
3. Department Civil and Environment Engineering, University of Ulsan, South Korea
Abstract
COVID-19 is extremely contagious and has brought serious harm to the world. Many researchers are actively involved in the study of rapid and reliable diagnostic methods for COVID-19. The study proposes a novel approach to COVID-19 diagnosis. The multiple-distance gray-level co-occurrence matrix (MDGLCM) was used to analyze chest CT images, the GA algorithm was used as an optimizer, and the feedforward neural network was used as a classifier. The results of 10 runs of 10-fold cross-validation show that the proposed method has a sensitivity of 83.38±1.40, a specificity of 81.15±2.08, a precision of 81.59±1.57, an accuracy of 82.26±0.96, an F1-score of 82.46±0.88, an MCC of 64.57±1.90, and an FMI of 82.47±0.88. The proposed MDGLCM-GA-based COVID-19 diagnosis method outperforms the other six state-of-the-art methods.
Publisher
IGI Global
Subject
General Earth and Planetary Sciences,General Environmental Science
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