Highly efficient stacking ensemble learning model for automated keratoconus screening
By combining the strengths of diverse base models and key Pentacam indices, the stacking ensemble approach ensures reliable, accurate KC screening, providing clinicians with an automated tool for early detection and better patient management.
Authors
- Zahra J. Muhsin (ORCID: https://orcid.org/0000-0003-3309-1706)
- Mo’ath AlShawabkeh (ORCID: https://orcid.org/0000-0003-0135-0267)
- Rami Qahwaji
- Majid A. Al-Taee (ORCID: https://orcid.org/0000-0002-3252-3637)
- Ibrahim Ghafir (ORCID: https://orcid.org/0000-0003-3702-3866)
- Muawyah Al Bdour (ORCID: https://orcid.org/0000-0003-3191-9047)
- Saif Aldeen AlRyalat (ORCID: https://orcid.org/0000-0001-5588-9458)
Institutions
- University of Jordan (JO)
- University of Bradford (GB)
- Hashemite University (JO)
- Oldham Council (GB)
Publication Details
- Journal
- Eye and Vision
- Published
- 2025-06-24
- DOI
- https://doi.org/10.1186/s40662-025-00440-6
- Citations
- 3
- Primary Topic
- Corneal surgery and disorders
- Type
- article
- Field-Weighted Citation Impact
- 4.08