Utilizing the ensemble of deep learning approaches to identify monkeypox disease

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dc.authorid0000-0002-1190-2849en_US
dc.authorid0000-0002-1897-9830en_US
dc.authorid0000-0002-9368-8902en_US
dc.contributor.authorÖrenç, Sedat
dc.contributor.authorAcar, Emrullah
dc.contributor.authorÖzerdem, Mehmet Siraç
dc.date.accessioned2023-03-02T05:50:12Z
dc.date.available2023-03-02T05:50:12Z
dc.date.issued2022en_US
dc.departmentDicle Üniversitesi, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractRecently, 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.en_US
dc.identifier.citationÖrenç, 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.en_US
dc.identifier.doi10.24012/dumf.1199679
dc.identifier.endpage691en_US
dc.identifier.issn1309-8640
dc.identifier.issn2146-4391
dc.identifier.issue4en_US
dc.identifier.startpage685en_US
dc.identifier.urihttps://dergipark.org.tr/tr/download/article-file/2752344
dc.identifier.urihttps://hdl.handle.net/11468/11298
dc.identifier.volume13en_US
dc.institutionauthorÖzerdem, Mehmet Siraç
dc.language.isoenen_US
dc.publisherDicle Üniversitesi Mühendislik Fakültesien_US
dc.relation.ispartofDicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMonkeypoxen_US
dc.subjectDeep learningen_US
dc.subjectClassificationen_US
dc.titleUtilizing the ensemble of deep learning approaches to identify monkeypox diseaseen_US
dc.titleUtilizing the ensemble of deep learning approaches to identify monkeypox disease
dc.typeArticleen_US

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