Comparison of Texture Feature Extraction Method for COVID-19 Detection With Deep Learning

Dionisius Adianto Tirta Nugraha, Aulia M.T. Nasution*

*Corresponding author for this work

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

1 Citation (Scopus)

Abstract

This paper describes research on texture feature extraction for COVID-19 detection. Fractal Dimension Texture Analysis (FDTA) and Gray Level Co-occurrence Matrix (GLCM) were used for feature extraction. A dense neural network is used for classification. Three classes were used for classification to classify Normal, COVID-19, and Other pneumonia. The data entered in the texture feature extraction is a chest x-ray (CXR) image that is grey scaled and resized into 400400 pixels. Performance analysis of the model uses a confusion matrix. The best performance feature extraction method for detecting COVID-19 is FDTA, with an accuracy testing of 62.5%.

Original languageEnglish
Title of host publicationProceedings - 2022 IEEE International Conference on Cybernetics and Computational Intelligence, CyberneticsCom 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages393-397
Number of pages5
ISBN (Electronic)9781665497428
DOIs
Publication statusPublished - 2022
Event6th IEEE International Conference on Cybernetics and Computational Intelligence, CyberneticsCom 2022 - Virtual, Malang, Indonesia
Duration: 16 Jun 202218 Jun 2022

Publication series

NameProceedings - 2022 IEEE International Conference on Cybernetics and Computational Intelligence, CyberneticsCom 2022

Conference

Conference6th IEEE International Conference on Cybernetics and Computational Intelligence, CyberneticsCom 2022
Country/TerritoryIndonesia
CityVirtual, Malang
Period16/06/2218/06/22

Keywords

  • COVID-19
  • Chest X-ray
  • FDTA
  • GLCM

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