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

Annisa Utama Berliana, Alhadi Bustamam

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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-Rayand 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 dataset, 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.

Original languageEnglish
Title of host publication2020 3rd International Conference on Information and Communications Technology, ICOIACT 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages148-152
Number of pages5
ISBN (Electronic)9781728173566
DOIs
Publication statusPublished - 24 Nov 2020
Event3rd International Conference on Information and Communications Technology, ICOIACT 2020 - Yogyakarta, Indonesia
Duration: 24 Nov 202025 Nov 2020

Publication series

Name2020 3rd International Conference on Information and Communications Technology, ICOIACT 2020

Conference

Conference3rd International Conference on Information and Communications Technology, ICOIACT 2020
CountryIndonesia
CityYogyakarta
Period24/11/2025/11/20

Keywords

  • and K-Nearest Neighbors (KNN)
  • chest X-ray
  • Classification
  • COVID-19
  • CT Image
  • Ensemble learning
  • Gabor Feature
  • Random Forest (RF)
  • Stacking
  • Support Vector Classification (SVC)

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