Utilizing the ensemble of deep learning approaches to identify monkeypox disease
Yükleniyor...
Tarih
2022
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Dicle Üniversitesi Mühendislik Fakültesi
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Recently, the monkeypox disease spreads to many countries rapidly and it becomes a serious health
problem. There are several symptoms that decrease the quality of the life. These symptoms must be
overcome to detect monkeypox disease in earlier stages. Therefore, it is crucial to decrease the spread rate
with the quick determination of the disease. In this study, it is aimed to identify monkeypox disease from
images datasets obtained from Kaggle by using Convolutional Neural Network models. These models are
named EfficientNetB3, ResNet50, and InceptionV3 respectively. According to the results of the three
models, resNet50 is the best model when they compare aspects of performance. The accuracy of resNet50
is %94,00 therefore it has highest accuracy value. There are four parameters to evaluate the performance
of the models. They are called as precision, recall, F1-score, and accuracy. These models demonstrate that
monkeypox can be classified with high precision. Therefore these models can be used for the future of the
work.
Açıklama
Anahtar Kelimeler
Monkeypox, Deep learning, Classification
Kaynak
Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi
WoS Q Değeri
Scopus Q Değeri
Cilt
13
Sayı
4
Künye
Örenç, S., Acar, E. ve Özerdem, M. S. (2022). Utilizing the ensemble of deep learning approaches to identify monkeypox disease. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 13(4), 685-691.
