Two-stage ensemble learning framework for automated classification of keratoconus severity.

Journal: Computers in biology and medicine
Published Date:

Abstract

BACKGROUND AND OBJECTIVE: Accurate staging of keratoconus (KC) is crucial for timely intervention and improving patient quality of life. Unlike prior studies that relied on traditional base machine learning (ML) models, this paper proposes a more advanced two-stage ensemble learning model, designed to automate KC severity staging and track disease progression with improved performance.

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 AlRyalat
    School of Medicine, The University of Jordan, Amman, Jordan.
  • Majid Al-Taee
    , Liverpool, UK.

Keywords

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