Utilizing the ensemble of deep learning approaches to identify monkeypox disease

Abstract

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.