Identify Covid-19 (Corona Virus Disease 20219) with Chest X-Ray Photos Using Artificial Neural Network

Abstract

Identification of lung abnormalities indicated by Covid-19 (Corona Virus Disease 2019) requires thoroughness and accuracy in decision making. Because it is related to the determination of the next follow-up to take appropriate action or treatment for the patient being treated. The study was raised to identify the lungs seen from chest X-rays whether normal or affected by Covid-19. Identification is made consists of several processes, namely pre-processing, characteristic extraction, and identification. Identify by comparing GLCM (Gray-Level Co-occurrence Matrix) and Statistical feature extraction. Pre-processing is used to improve the quality of X-ray photos including in this study, namely resize and grayscale. The characteristic extraction process is used to obtain input data that will be used when used in the identification process. Extraction of features here using two methods, namely: GLCM and Statistics. The GLCM methods used are Contrast, Correlation, Energy, Homogenity. As for statistics the values sought include Mean, Deviation, Skewness, Energy, Entropy, Smoothness. Identification using the Artificial Neural Network Radial Basis Function (ANN-RBF). The latter process sought the accuracy levels of two characteristic extraction methods (GLCM and Statistics) identified with ANN-RBF. The level of accuracy sought by using K-Fold Cross Validation. The data used a total of 50 data, with details of 25 chest X-rays that have been identified as normal and 25 chest X-rays identified as Covid-19. The results showed that the extraction of traits using the GLCM method was better viewed from its accuracy rate when compared to statistical methods, with each accuracy rate for GLCM at 91.63% and Statistics at 87.08%.