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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Patrick Kwabena Mensah
University of Energy and Natural Resources
Computer Science & IT Engineering

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Adaptive binary capsule network for complex image recognition Mavis Serwaa Yeboah; Patrick Kwabena Mensah; Adebayo Felix Adekoya; Mighty Abra Ayidzoe
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.pp3745-3760

Abstract

Glaucoma and cataracts are leading causes of blindness worldwide, emphasizing the need for early detection. Convolutional neural networks (CNNs) have shown promise in detecting ocular diseases, but require extensive training datasets. However, medical datasets are scarce, limited, and imbalanced, prompting the use of time-consuming data augmentation techniques. To address these limitations, a computationally efficient and robust capsule network (CapsNet) model was proposed. The model features a novel adaptive contrast spatial filtering (ACSF) algorithm and incorporates a local binary pattern (LBP) algorithm to enhance robustness. This study achieved good recognition accuracy, with scores of 96.90% on the combined dataset, 96.45% on the cataract-only dataset, and 96.78% on the glaucoma-only dataset. The model's performance is comparable to state-of-the-art models, demonstrating its potential to support ophthalmologists in diagnosing cataracts and glaucoma-related eye issues.
Co-Authors Adebayo Felix Adekoya Mavis Serwaa Yeboah Mighty Abra Ayidzoe