Accurate diagnosis of monkeypox is challenging because it is a viral disease that causes skin lesions resembling other dermatological conditions. This study aims to present a machine learning-based approach for the classification of monkeypox skin lesions using image-based feature extraction techniques. In this study, texture-based features were extracted from skin lesion images using the Gray-Level Co-Occurrence Matrix (GLCM), Gray-Level Run Length Matrix (GLRLM), Local Binary Patterns (LBP), and Focus Measures for image sharpness analysis to improve classification performance. To address class imbalance and enhance model reliability, the Synthetic Minority Oversampling Technique (SMOTE) was applied. Four different classifiers Random Forest (RF), K-Nearest Neighbors (KNN), Naive Bayes (NB) and Decision Tree (DT) were evaluated using accuracy, precision, recall, specificity, F1-score and area under the curve (AUC) metrics. The results demonstrated that the highest classification performance was achieved by the Random Forest classifier, with an accuracy of 90.02% and an AUC of 96.10%, followed by the KNN classifier. Among the extracted features, LBP and Focus Measures provided the most discriminative information for monkeypox lesion classification. Overall, feature extraction and data balancing significantly improved classification performance. The findings indicate that machine learning-based classification combined with texture feature extraction and data balancing is effective for detecting monkeypox skin lesions. RF and KNN emerged as the most suitable classifiers among those tested. To the best of our knowledge, this is the first study to investigate monkeypox lesion classification using the specified extracted features. These results highlight the potential of image-based machine learning methods in supporting early diagnosis, while future studies should focus on larger datasets and hybrid deep learning approaches to further improve accuracy and generalizability.
Şentürk et al. (Mon,) studied this question.