Comparison of Texture Feature Extraction Method for COVID-19 Detection With Deep Learning
6th IEEE International Conference on Cybernetics and Computational Intelligence, CyberneticsCom 2022
; : 393-397, 2022.
Article
in English
| Scopus | ID: covidwho-2051962
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%. © 2022 IEEE.
Chest X-ray; COVID-19; FDTA; GLCM; Deep learning; Extraction; Feature extraction; Fractal dimension; Textures; Chest X-ray image; Feature extraction methods; Features extraction; Fractal dimension texture analyse; Gray-level co-occurrence matrix; Grey-level co-occurrence matrixes; Neural-networks; Texture analysis; Texture feature extraction
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
6th IEEE International Conference on Cybernetics and Computational Intelligence, CyberneticsCom 2022
Year:
2022
Document Type:
Article
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