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

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%. © 2023 International Journal of Artificial Intelligence.

COVID-19 Classification for Chest X-Ray Images using Deep Learning and Resnet-101

The catastrophic spread of the COVID-19 virus has began in December 2019 which first originated in Wuhan, China and spread rapidly throughout the world. The way to break the chain of spread of the virus is by detecting it using a tool called swab and polymerase chain reaction (PCR), but the price of these tools is expensive and the waiting time is long relatively. This study uses Deep learning as an image recognition method with CNN architecture. X-ray images are used as material to identify infected patients with COVID-19 or normal. The total number of x-ray images is 2562 which is divided into 2 classes, positive and normal. The COVID-19 x-ray image will also use CLAHE preprocessing and two sets of data that will be used as deep learning training materials, original data and CLAHE preprocessing data. The training process is conducted using CNN with the Resnet-101 architecture. the experiment divided the data with the ratio of training data and test data of 80:20. The confusion matrix shows the proposed method provides the highest classification performance with 99.61% accuracy, 99.62% sensitivity and 99.60% specificity. © 2021 IEEE.