Classification of Orthopoxvirus with Deep Learning in Reduced Data Scenarios Using Resampling Techniques

Abstract

[Abstract] In recent years, monkeypox has become a growing global threat, where early diagnosis is essential for its control. This work explores the use of Deep Learning techniques applied to skin image analysis to improve the detection and classification of this disease compared to other similar ones. A highly unbalanced dataset of 770 images is used, so resampling techniques such as SMOTE and SMOTEENN are applied. The objective is not only to compare the performance of different Deep Learning models, but also to measure the impact on classification produced by the use of resampling strategies. It also seeks to identify the best combination to support automatic diagnosis in clinical and epidemiological contexts.