Combination of gray level co-occurrence matrix and artificial neural networks for classification of COVID-19 based on chest X-ray images - Imran, Bahtiar; Delsi Samsumar, Lalu; Subki, Ahmad; Zaeniah, Zaeniah; Salman, Salman; Rijal Alfian, Muhammad (IAES International Journal of Artificial Intelligence (IJ-AI), 2024)
This research uses the gray level co-occurrence matrix (GLCM) and artificial neural networks to classify COVID-19 images based on chest X-ray images. According to previous studies, there has never been a researcher who has integrated GLCM with artificial neural networks. Epochs 10, 30, 50, 70, 100, and 120 were used in this research. The total number of data points used in this investigation was 600, divided into 300 normal chests and 300 COVID-19 data points. Epoch 10 had 91% accuracy, epoch 30 had 91% accuracy, epoch 50 had 92% accuracy, epoch 70 had 91% accuracy, epoch 100 had 92% accuracy, and epoch 120 had 90% accuracy in categorization. As indicated by the results of the classification tests, combining GLCM and artificial neural networks can produce good results; a combination of these methods can yield a classification for COVID-19.