Highly efficient stacking ensemble learning model for automated keratoconus screening.

Journal: Eye and vision (London, England)
Published Date: Jun 24, 2025

Abstract

BACKGROUND: Despite extensive research on keratoconus (KC) detection with traditional machine learning models, stacking ensemble learning approaches remain underexplored. This paper presents a stacking ensemble learning method to enhance automated KC screening.

Authors

  • Zahra J Muhsin
    Faculty of Engineering and Digital Technologies, University of Bradford, Bradford, BD7 1DP, UK.
  • Rami Qahwaji
    Faculty of Engineering and Digital Technologies, University of Bradford, Bradford, BD7 1DP, UK. r.s.r.qahwaji@bradford.ac.uk.
  • Ibrahim Ghafir
    Faculty of Engineering and Digital Technologies, University of Bradford, Bradford, BD7 1DP, UK.
  • Mo'ath AlShawabkeh
    Department of Ophthalmology, The Hashemite University, Zarqa, Jordan.
  • Muawyah Al Bdour
    Department of Ophthalmology, The University of Jordan, Amman, Jordan.
  • Saif Aldeen AlRyalat
    Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, CO, United States.
  • Majid Al-Taee
    , Liverpool, UK.

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