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Advanced Classification of Poxvirus-Based Skin Diseases Using Deep Learning Techniques

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Publicado en:Traitement du Signal vol. 42, no. 5 (Oct 2025), p. 2777-2787
Autor principal: Kaan Arik
Otros Autores: Ağdaş, Mehmet T, Korkmaz, Adem, Koşunalp, Selahattin, Iliev, Teodor
Publicado:
International Information and Engineering Technology Association (IIETA)
Materias:
Africa
Infectious diseases
COVID-19 vaccines
Deep learning
Classification
Mortality
Medical imaging
Skin diseases
Lesions
Smallpox
Fever
Disease prevention
Immunization
Accuracy
Datasets
Image analysis
Artificial intelligence
Mpox
Pandemics
Epidemics
Measles
Public health
Viruses
Object recognition
Viral infections
Real time
Disease transmission
Acceso en línea:Citation/Abstract
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Resumen: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.
ISSN:0765-0019
1958-5608
DOI:10.18280/ts.420528
Fuente:Engineering Database