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

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.

Artificial intelligence-based Karawo motif formation using genetic algorithm

“…The survivor selection or replacement process carefully manages the retention of individuals for the next generation. Finally, the iterative process continues until the termination criteria are met, ensuring the algorithm converges to an optimal solution or a predefined endpoint [20] [24].…”