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. 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.

Detection of Various Diseases in Fruits and Vegetables with the Help of Different Deep Learning Techniques

“…There are four parameters to evaluate the performance of the models. These; F-1 score, recall, accuracy and precision [13][14]. Test results and performance metrics of the CNN and ResNet50 models are as in Table I.…”