Detection of SARS-CoV-2 Main Variants of Concerns using Deep Learning
2022 Innovations in Intelligent Systems and Applications Conference, ASYU 2022, Antalya, Türkiye, 7 - 09 Eylül 2022, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Doi Numarası: 10.1109/asyu56188.2022.9925559
- Basıldığı Şehir: Antalya
- Basıldığı Ülke: Türkiye
- Anahtar Kelimeler: classifiers, COVID-19, deep learning, SARS-CoV-2 variants
- Ankara Yıldırım Beyazıt Üniversitesi Adresli: Evet
Özet
© 2022 IEEE.The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been a big effect on worldwide and is responsible for the coronavirus disease 2019 (COVID-19). The novel viral variants of the virus have been detected due to the high mutation rate of the virus. Although some SARS-Co V-2 variants can pose a significant risk for public health since they reduce the effect of the vaccine and medicine, some variants have a little effect on the health. The variants of SARS-Co V-2 may be classified in two parts, which are variants of concern and variants of interest. Variants of concern significantly decrease the effect of vaccines as well as increase the fatality. Five main SARS-CoV-2 variants of concerns, which are B.1.1.7, B.1.351, P.1, B.1.617.2, and B.1.1.529 are detected so far and it is crucial to identify these variants among the others. In this study, we propose a deep learning method to detect main SARS-CoV-2 variants of concerns from human genome sequences. In the proposed approach, first, features separating SARS-Co V-2 main variants of concerns are extracted using deep learning. Second, machine learning classsifiers, k-nearest neighbor, multilayer perceptron, random forest, and support vector machine are performed to detect the variants. The proposed method exhibits admirable results and achieves an accuracy of 99.99 %.