Classification of covid-19 omicron variant using hybrid deep transfer learning based on x-ray chest images
Yükleniyor...
Dosyalar
Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Altınbaş Üniversitesi
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
In 2019, the first case of COVID-19 was announced in China in Wuhan Province. Which led to the panic
of the world and the declaration of a state of extreme emergency by the World Health Organization.
Given that the world was in a state of crisis and closure, the use of deep learning technology provides
speed and accuracy in diagnosing disease through chest images. Therefore, in this study, the dental
X-Ray images of people infected with the omicron strain of Covid-19 virus were classified in comparison
with a group of healthy people. In this study, we used 4 types of pre-trained deep learning algorithms
in two ways, the first is using cross-validation and the second is the hybrid method by extracting the
features from the models and then applying them to two types of deep learning algorithms (SVM and
KNN). Accuracy results were obtained in the first scenario with a percentage of 94%, while in the second
scenario, the accuracy results in the SVM classifier are higher than KNN with a difference of 5%, which
is 92%. We also compared studies that used X-Ray images to classify COVID-19, as our results showed a
clear superiority compared to other studies.
Açıklama
Anahtar Kelimeler
Classification, CNN, SVM, KNN, Deep Transfer Learning, Feature Extraction
Kaynak
AURUM Journal of Health Sciences
WoS Q Değeri
Scopus Q Değeri
Cilt
4
Sayı
3
Künye
Al-Jumaili, S. (2022). Classification of covid-19 omicron variant using hybrid deep transfer learning based on x-ray chest images. AURUM Journal of Health Sciences, 4(3), 153-165.
