An optimized ensemble grey wolf-based pipeline for monkeypox diagnosis
Abstract As the world recovered from the coronavirus, the emergence of the monkeypox virus signaled a potential new pandemic, highlighting the need for faster and more efficient diagnostic methods. This study introduces a hybrid architecture for automatic monkeypox diagnosis by leveraging a modified...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-87455-0 |
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| author | Ahmed I. Saleh Asmaa H. Rabie Shimaa E. ElSayyad Ali E. Takieldeen Fahmi Khalifa |
| author_facet | Ahmed I. Saleh Asmaa H. Rabie Shimaa E. ElSayyad Ali E. Takieldeen Fahmi Khalifa |
| author_sort | Ahmed I. Saleh |
| collection | DOAJ |
| description | Abstract As the world recovered from the coronavirus, the emergence of the monkeypox virus signaled a potential new pandemic, highlighting the need for faster and more efficient diagnostic methods. This study introduces a hybrid architecture for automatic monkeypox diagnosis by leveraging a modified grey wolf optimization model for effective feature selection and weighting. Additionally, the system uses an ensemble of classifiers, incorporating confusion based voting scheme to combine salient data features. Evaluation on public data sets, at various of training samples percentages, showed that the proposed strategy achieves promising performance. Namely, the system yielded an overall accuracy of 98.91% with testing run time of 5.5 seconds, while using machine classifiers with small number of hyper-parameters. Additional experimental comparison reveals superior performance of the proposed system over literature approaches using various metrics. Statistical analysis also confirmed that the proposed AMDS outperformed other models after running 50 times. Finally, the generalizability of the proposed model is evaluated by testing its performance on external data sets for monkeypox and COVID-19. Our model achieved an overall diagnostic accuracy of 98.00% and 99.00% on external COVID and monkeypox data sets, respectively. |
| format | Article |
| id | doaj-art-0cdfa68652b5446c9f02981aad40c7a4 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-0cdfa68652b5446c9f02981aad40c7a42025-02-02T12:19:23ZengNature PortfolioScientific Reports2045-23222025-01-0115112310.1038/s41598-025-87455-0An optimized ensemble grey wolf-based pipeline for monkeypox diagnosisAhmed I. Saleh0Asmaa H. Rabie1Shimaa E. ElSayyad2Ali E. Takieldeen3Fahmi Khalifa4Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura UniversityComputers and Control Systems Engineering Department, Faculty of Engineering, Mansoura UniversityComputers and Control Systems Engineering Department, Faculty of Engineering, Mansoura UniversityFaculty of Artificial Intelligence, Delta University for Science and TechnologyElectronics and Communication Engineering Department, Mansoura UniversityAbstract As the world recovered from the coronavirus, the emergence of the monkeypox virus signaled a potential new pandemic, highlighting the need for faster and more efficient diagnostic methods. This study introduces a hybrid architecture for automatic monkeypox diagnosis by leveraging a modified grey wolf optimization model for effective feature selection and weighting. Additionally, the system uses an ensemble of classifiers, incorporating confusion based voting scheme to combine salient data features. Evaluation on public data sets, at various of training samples percentages, showed that the proposed strategy achieves promising performance. Namely, the system yielded an overall accuracy of 98.91% with testing run time of 5.5 seconds, while using machine classifiers with small number of hyper-parameters. Additional experimental comparison reveals superior performance of the proposed system over literature approaches using various metrics. Statistical analysis also confirmed that the proposed AMDS outperformed other models after running 50 times. Finally, the generalizability of the proposed model is evaluated by testing its performance on external data sets for monkeypox and COVID-19. Our model achieved an overall diagnostic accuracy of 98.00% and 99.00% on external COVID and monkeypox data sets, respectively.https://doi.org/10.1038/s41598-025-87455-0Grey WolfMonkeypoxNeural networkEnsemble classierConfusion-based voting |
| spellingShingle | Ahmed I. Saleh Asmaa H. Rabie Shimaa E. ElSayyad Ali E. Takieldeen Fahmi Khalifa An optimized ensemble grey wolf-based pipeline for monkeypox diagnosis Scientific Reports Grey Wolf Monkeypox Neural network Ensemble classier Confusion-based voting |
| title | An optimized ensemble grey wolf-based pipeline for monkeypox diagnosis |
| title_full | An optimized ensemble grey wolf-based pipeline for monkeypox diagnosis |
| title_fullStr | An optimized ensemble grey wolf-based pipeline for monkeypox diagnosis |
| title_full_unstemmed | An optimized ensemble grey wolf-based pipeline for monkeypox diagnosis |
| title_short | An optimized ensemble grey wolf-based pipeline for monkeypox diagnosis |
| title_sort | optimized ensemble grey wolf based pipeline for monkeypox diagnosis |
| topic | Grey Wolf Monkeypox Neural network Ensemble classier Confusion-based voting |
| url | https://doi.org/10.1038/s41598-025-87455-0 |
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