Implementation of Stacking Ensemble Learning for Classification of COVID-19 using Image Dataset CT Scan and Lung X-Ray

Novel Coronavirus Disease (COVID-19) is a disease caused by SARS-CoV-2, which has become a global pandemic. COVID-19 was first discovered in Wuhan, China, and has already spread to various countries, which, until now, still haven't found a proper way to deal with it. Various studies related to COVID-19 have been carried out, including initial screening to control the disease's spread. X-ray images and Computed Tomography (CT) can be utilized for initial screening in diagnosing lung conditions for patients with COVID-19 symptoms. Machine learning has been at the forefront of many fields, such as analyzing X-Ray and CT Images. Machine learning shows an outstanding performance compared to other methods. In this paper, we present an ensemble learning with stacking to analyze X-Ray and CT in calcifying COVID-19, which was previously pre-documented using the Gabor feature. The ensemble learning model is built with two levels of learning, namely the base-learners and the meta-learner. The base-learners we use to build the model are Support Vector Classification (SVC), Random Forest (RF), and K-Nearest Neighbors (KNN), and the meta-learner we use is Support Vector Classification (SVC). The proposed method's performance is implemented on a publicly available COVID-19 data set, including 1140 chest X-Ray images and 2400 CT Images. The proposed method shows that the stacking ensemble learning of Support Vector Classification (SVC), Random Forest (RF), and K-Nearest Neighbors (KNN) can provide accuracy above 97% for CT Images and 99% for chest X-Ray images.