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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Indah Sinthya Permata Sari
Gunadarma University
Computer Science & IT Engineering

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Efficient deep learning for automated corneal ulcer severity classification from fluorescein images Rodiah Rodiah; Indah Sinthya Permata Sari; Matrissya Hermita; Sarifuddin Madenda; Diana Tri Susetianingtias
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3603-3613

Abstract

Corneal ulcers can cause permanent vision loss if not diagnosed and managed promptly, particularly in settings with limited access to ophthalmology services. This study aims to develop an automated deep learning approach for classifying corneal ulcer severity from fluorescein slit-lamp images. An EfficientNetV2-S–based model is employed, incorporating corneal area masking to suppress non-relevant regions and class distribution–based augmentation to address data imbalance. To improve evaluation reliability, a leakage-aware data splitting strategy is applied before and after augmentation. Experimental results show that the proposed approach achieves a maximum validation accuracy of 95.93% under non-leakage conditions for the category classification scenario, while maintaining high training efficiency. These results demonstrate that the proposed method provides a robust and efficient solution for automated corneal ulcer severity assessment and has the potential to support clinical decision-making in ophthalmic practice.
Co-Authors Matrissya Hermita Rodiah Rodiah Sarifuddin Madenda Susetianingtias, Diana Tri