A Proposed Approach for Monkeypox Classification

Abstract

Public health concerns have been heightened by the emergence and spread of monkeypox, a viral disease that affects both humans and animals. The significance of early detection and diagnosis of monkeypox cannot be overstated, as it plays a crucial role in minimizing the negative impact on affected individuals and safeguarding public health. Monkeypox poses a considerable threat to human well-being, causing physical discomfort and mental distress, while also posing challenges to work productivity. This study proposes an applied model that combines deep learning models such as: ResNet-50, VGG16, MobileNet and machine learning models such as: Random Forest Classifier, K-Nearest Neighbors Classifier, Gaussian Naive Bayes Classifier, Decision Tree Classifier, Logistic Regression Classifier, AdaBoost Classifier to classify and detect monkeypox. The datasets are used in this research are the Monkeypox Skin Lesion Dataset (MSLD) and the Monkeypox Image Dataset (MID) that have total 659. Subjects range from healthy cases to severe skin lesions. The test results show that the model which combines deep learning and machine learning models achieves positive results, with Accuracy being 0.97 and F1-score being 0.98.