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Utilizing the Ensemble of Deep Learning Approaches to Identify Monkeypox Disease
Öz
Recently, the monkeypox disease spreads to many countries rapidly and it becomes a serious health problem. In addition, this disease affects the quality of a person's life. Therefore, it is crucial to decrease the spread rate with the quick determination of the disease. In order to identify monkeypox rapidly, deep learning models are used. They 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 sets %94.00. There are four parameters that are used to evaluate the performance of the models. There are called precision, recall, f1-score, and support. These models demonstrate that monkeypox can be classified with high precision. Therefore these models can be used for the future of the work.
Anahtar Kelimeler
Kaynakça
- World Health Organization. (2022). Monkeypox outbreak 2022 - Global. https://www.who.int/emergencies/situations/monkeypoxoubreak-2022
Ayrıntılar
Birincil Dil
İngilizce
Konular
-
Bölüm
Araştırma Makalesi
Yayımlanma Tarihi
3 Ocak 2023
Gönderilme Tarihi
4 Kasım 2022
Kabul Tarihi
2 Ocak 2023
Yayımlandığı Sayı
Yıl 2022 Cilt: 13 Sayı: 4
APA
Örenç, S., Acar, E., & Özerdem, M. S. (2023). Utilizing the Ensemble of Deep Learning Approaches to Identify Monkeypox Disease. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 13(4), 685-691. https://doi.org/10.24012/dumf.1199679
AMA
1.Örenç S, Acar E, Özerdem MS. Utilizing the Ensemble of Deep Learning Approaches to Identify Monkeypox Disease. DÜMF MD. 2023;13(4):685-691. doi:10.24012/dumf.1199679
Chicago
Örenç, Sedat, Emrullah Acar, ve Mehmet Siraç Özerdem. 2023. “Utilizing the Ensemble of Deep Learning Approaches to Identify Monkeypox Disease”. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi 13 (4): 685-91. https://doi.org/10.24012/dumf.1199679.
EndNote
Örenç S, Acar E, Özerdem MS (01 Ocak 2023) Utilizing the Ensemble of Deep Learning Approaches to Identify Monkeypox Disease. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi 13 4 685–691.
IEEE
[1]S. Örenç, E. Acar, ve M. S. Özerdem, “Utilizing the Ensemble of Deep Learning Approaches to Identify Monkeypox Disease”, DÜMF MD, c. 13, sy 4, ss. 685–691, Oca. 2023, doi: 10.24012/dumf.1199679.
ISNAD
Örenç, Sedat - Acar, Emrullah - Özerdem, Mehmet Siraç. “Utilizing the Ensemble of Deep Learning Approaches to Identify Monkeypox Disease”. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi 13/4 (01 Ocak 2023): 685-691. https://doi.org/10.24012/dumf.1199679.
JAMA
1.Örenç S, Acar E, Özerdem MS. Utilizing the Ensemble of Deep Learning Approaches to Identify Monkeypox Disease. DÜMF MD. 2023;13:685–691.
MLA
Örenç, Sedat, vd. “Utilizing the Ensemble of Deep Learning Approaches to Identify Monkeypox Disease”. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, c. 13, sy 4, Ocak 2023, ss. 685-91, doi:10.24012/dumf.1199679.
Vancouver
1.Sedat Örenç, Emrullah Acar, Mehmet Siraç Özerdem. Utilizing the Ensemble of Deep Learning Approaches to Identify Monkeypox Disease. DÜMF MD. 01 Ocak 2023;13(4):685-91. doi:10.24012/dumf.1199679
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