Covid-19 Detection on Chest CT-Scan Image Using GLCM-Based Feature Extraction with K-NN and Naïve Bayes Classification

Abstract

- Covid-19 is a virus that has spread and become a global pandemic. This virus infected the vital human organ, which is the lungs. Therefore, this research identified Covid-19 and non-covid-19 diseases based on chest CT-Scan images using K-NN and Naïve Bayes classification methods. The system is constructed through pre-processing, segmentation, GLCM-based feature extraction, and dividing the testing and training data with K-fold cross-validation with the value of 5 and 10, then evaluated using Confusion Matrix. The algorithm accuracy value from the K-NN classification model is obtained as 99,6% and Naïve Bayes got the value of 93,5%. In comparison, the K-NN method obtained the highest sensitivity level with a value of 100% and a specificity value of 98.4% for the two methods used. In this test, the K-NN classifier method is more appropriate than the Naïve Bayes method because some features of GLCM