Early detection of human Mpox: A comparative study by using machine learning and deep learning models with ensemble approach
The integration of ML and DL models in an ensemble framework significantly enhances Mpox detection. This AI-driven diagnostic approach offers a scalable, accurate, and efficient solution, particularly in resource-limited settings. Future research will focus on improving model interpretability, federated learning integration, and validation with real-world clinical data.
Authors
- Binapani Barik
- Ranjan K. Mohapatra (ORCID: https://orcid.org/0000-0001-7623-3343)
- Lawrence Sena Tuglo (ORCID: https://orcid.org/0000-0001-8695-2384)
- Himansu Bhusan Samal (ORCID: https://orcid.org/0000-0002-0803-6847)
- Alok Patel
- Salah M. El‐Bahy (ORCID: https://orcid.org/0000-0002-1652-3522)
- Francesco Branda (ORCID: https://orcid.org/0000-0002-9485-3877)
- Ashish K. Sarangi (ORCID: https://orcid.org/0000-0002-5602-4736)
- Adel Alkhedaide (ORCID: https://orcid.org/0000-0002-5008-8421)
- Lizaranee Tripathy
- Madhumita Pal (ORCID: https://orcid.org/0000-0003-0680-3747)
- Mona Youssef
Institutions
- Università Campus Bio-Medico (IT)
- Taif University (SA)
- Benha University (EG)
- University of Health and Allied Sciences (GH)
- Government College of Engineering, Keonjhar (IN)
- Centurion University of Technology and Management (IN)
Publication Details
- Journal
- Digital Health
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1177/20552076251344135
- Citations
- 5
- Primary Topic
- Poxvirus research and outbreaks
- Type
- article
- Field-Weighted Citation Impact
- 5.18
Funders
- Taif University