التفاصيل البيبلوغرافية
| العنوان: |
Identification and classification of SARS-CoV-2 on chest CT-scan image using GLCM-based feature extraction with K-NN and naïve bayes methods. |
| المؤلفون: |
Rachman, Rezky Rachmadany, Dewang, Syamsir, Ilyas, Sri Dewi Astuty, Juarlin, Eko |
| المصدر: |
AIP Conference Proceedings; 2024, Vol. 2774 Issue 1, p1-10, 10p |
| مصطلحات موضوعية: |
ORGANS (Anatomy), COVID-19, SARS-CoV-2, CLASSIFICATION, ALGORITHMS |
| مستخلص: |
Covid-19 is a virus that has spread and become a global pandemic. This virus infected the vital human organ, which is the lungs. Therefore, this research identified Covid-19 and non-covid-19 diseases based on chest CT-Scan images using K-NN and Naïve Bayes classification methods. The system is constructed through pre-processing, segmentation, GLCM-based feature extraction, and dividing the testing and training data with K-fold cross-validation with the value of 5 and 7, then evaluated using Confusion Matrix. The algorithm accuracy value from the K-NN classification model is obtained as 99,6% and Naïve Bayes got the value of 93,5%. In comparison, the K-NN method obtained the highest sensitivity level with a value of 100% and a specificity value of 98.4% for the two methods used. In this test, the K-NN classifier method is more appropriate than the Naïve Bayes method because some features of GLCM are more accommodating to the KNN classifier. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |
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