LBPSCN: Local Binary Pattern Scaled Capsule Network for the Recognition of Ocular Diseases

Glaucoma and cataracts are leading causes of blindness worldwide, resulting in significant vision loss and quality of life impairment. Early detection and diagnosis are crucial for effective treatment and prevention of further damage. However, diagnosis is challenging, especially when intraocular pressure is low or cataracts are present. Deep learning algorithms, particularly Convolutional Neural Networks (CNNs), have shown promise in detecting eye diseases but require large training datasets to achieve high performance.. To address this limitation, this work proposes a modified Capsule Network algorithm with a novel scaled processing algorithm and local binary pattern layer, enabling robust and accurate diagnosis of glaucoma and cataracts. The proposed model demonstrates performance comparable to state-of-the-art methods, achieving high accuracy on combined, cataract-only, and glaucoma-only datasets (94.32%, 96.87%, and 95.23%, respectively). This work introduces enhanced feature extraction and robustness to illumination variations, addressing critical limitations of existing methods.. The proposed model offers a promising tool for ophthalmologists and glaucoma specialists to accurately diagnose glaucoma and cataract-compromised eyes, potentially improving patient outcomes.

@article{Serwaa2024,
  title     = {LBPSCN: Local Binary Pattern Scaled Capsule Network for the Recognition of Ocular Diseases},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {6},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Mavis Serwaa and Patrick Kwabena Mensah and Adebayo Felix Adekoya and Mighty Abra Ayidzoe},
  doi       = {10.14569/IJACSA.2024.01506155},
  url       = {https://doi.org/10.14569/IJACSA.2024.01506155}
}