Advanced Classification of Poxvirus-Based Skin Diseases Using Deep Learning Techniques
Сохранить в:
| Опубликовано в:: | Traitement du Signal vol. 42, no. 5 (Oct 2025), p. 2777-2787 |
|---|---|
| Главный автор: | |
| Другие авторы: | , , , |
| Опубликовано: |
International Information and Engineering Technology Association (IIETA)
|
| Предметы: | |
| Online-ссылка: | Citation/Abstract Full Text - PDF |
| Метки: |
Нет меток, Требуется 1-ая метка записи!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3272277487 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 0765-0019 | ||
| 022 | |a 1958-5608 | ||
| 024 | 7 | |a 10.18280/ts.420528 |2 doi | |
| 035 | |a 3272277487 | ||
| 045 | 2 | |b d20251001 |b d20251031 | |
| 100 | 1 | |a Kaan Arik | |
| 245 | 1 | |a Advanced Classification of Poxvirus-Based Skin Diseases Using Deep Learning Techniques | |
| 260 | |b International Information and Engineering Technology Association (IIETA) |c Oct 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 651 | 4 | |a Africa | |
| 653 | |a Infectious diseases | ||
| 653 | |a COVID-19 vaccines | ||
| 653 | |a Deep learning | ||
| 653 | |a Classification | ||
| 653 | |a Mortality | ||
| 653 | |a Medical imaging | ||
| 653 | |a Skin diseases | ||
| 653 | |a Lesions | ||
| 653 | |a Smallpox | ||
| 653 | |a Fever | ||
| 653 | |a Disease prevention | ||
| 653 | |a Immunization | ||
| 653 | |a Accuracy | ||
| 653 | |a Datasets | ||
| 653 | |a Image analysis | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Mpox | ||
| 653 | |a Pandemics | ||
| 653 | |a Epidemics | ||
| 653 | |a Measles | ||
| 653 | |a Public health | ||
| 653 | |a Viruses | ||
| 653 | |a Object recognition | ||
| 653 | |a Viral infections | ||
| 653 | |a Real time | ||
| 653 | |a Disease transmission | ||
| 700 | 1 | |a Ağdaş, Mehmet T | |
| 700 | 1 | |a Korkmaz, Adem | |
| 700 | 1 | |a Koşunalp, Selahattin | |
| 700 | 1 | |a Iliev, Teodor | |
| 773 | 0 | |t Traitement du Signal |g vol. 42, no. 5 (Oct 2025), p. 2777-2787 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3272277487/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3272277487/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |