Sürdürülebilir Kalkınma Amaçları

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Yazarlar

• Agdas, Mehmet T.(1)
• Arik, Kaan(1)
• Iliev, Teodor(1)
• Korkmaz, Adem(1)
• Kosunalp, Selahattin(1)

• MakaleAçık Erişim2025/r/munzur/4120
  Advanced Classification of Poxvirus-Based Skin Diseases Using Deep Learning Techniques

  Arik, Kaan; Agdas, Mehmet T.; Korkmaz, Adem; Kosunalp, Selahattin; Iliev, Teodor

  Viral infections, especially those of the poxvirus family, present significant diagnostic challenges due to their similar clinical symptoms. This study proposes an innovative deep learning-based approach to classify six categories of poxvirus-related skin diseases: chickenpox, cowpox, healthy, measles, monkeypox, and smallpox. A dataset of 9,120 augmented images was used to train, validate, and test three advanced deep-learning models-YOLOv8, YOLOv5, and ResNet32. Among the models, YOLOv8 demonstrated superior performance, achieving an accuracy of 99.80%, precision of 99.28%, and recall of 99.14%, significantly outperforming YOLOv5 and ResNet32. The results underscore the potential of YOLOv8 in medical image analysis, providing a robust and efficient tool for the early detection and accurate classification of viral skin diseases. Comparisons with related studies highlight the effectiveness of the proposed approach, making it a state-of-the-art solution for improving diagnostic accuracy in healthcare. Future work will focus on extending the dataset and evaluating the model's applicability in real-time clinical environments.

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