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

2 Citations (Scopus)

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
Country/TerritoryIndonesia
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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