@inproceedings{ca95980207e34c9591527dfabc90d94f,
title = "Comparison of Texture Feature Extraction Method for COVID-19 Detection With Deep Learning",
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%.",
keywords = "COVID-19, Chest X-ray, FDTA, GLCM",
author = "Nugraha, {Dionisius Adianto Tirta} and Nasution, {Aulia M.T.}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 6th IEEE International Conference on Cybernetics and Computational Intelligence, CyberneticsCom 2022 ; Conference date: 16-06-2022 Through 18-06-2022",
year = "2022",
doi = "10.1109/CyberneticsCom55287.2022.9865582",
language = "English",
series = "Proceedings - 2022 IEEE International Conference on Cybernetics and Computational Intelligence, CyberneticsCom 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "393--397",
booktitle = "Proceedings - 2022 IEEE International Conference on Cybernetics and Computational Intelligence, CyberneticsCom 2022",
address = "United States",
}