Classification of Orthopoxvirus with Deep Learning in reduced data scenarios using resampling techniques

• Darío Santos ^[1] ; Daniel Rivero ^[1] ; Alejandro Puente-Castro ^[1]
  1. [1] Universidade da Coruña

     Universidade da Coruña

     A Coruña, España

     
• Localización: Proceedings XoveTIC 2025: Impulsando el talento científico / coord. por Manuel Lagos Rodríguez, Hilda Romero-Velo, Álvaro Leitao , Javier Pereira-Loureiro , Manuel Penedo , 2026, ISBN 978-84-9749-925-5, págs. 223-230
• Idioma: inglés
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• Resumen

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