Tree Seed Algorithm-Based Optimized Deep Features Selection for Glaucoma Disease Classification

Glaucoma is a common eye condition that can cause irreversible blindness if left untreated. Glaucoma can be identified by the optic nerve disorder (a perilous path that carries the potential risk) and leads to blindness. Therefore, early glaucoma detection is critical for optimizing treatment outcomes and preserving vision. The majority of afflicted people typically do not exhibit any overt symptoms. Since many afflicted people go untreated as a result, early detection is essential for successful therapy. Systems for detecting glaucoma have been developed through a great deal of research. These manual, time-consuming, and frequently erroneous traditional diagnostic methods are not suitable for glaucoma diagnosis thus, automated methods are required. This research study proposes a novel glaucoma diagnosis model that addresses the difficulty of determining the complex cup-to-disc ratio. For accurate feature extraction, a publicly available dataset with two classes (Glaucoma positive and negative) is utilized from Kaggle. The dataset is augmented using the Flip technique and resized. A two-step approach using the Mobilenetv2 model is used to extract features from positive and negative classes. Accurate features are selected with the help of Transfer Function Sequential Analysis (TSA). The enriched features are then classified using three different classifiers: Cubic SVM, Ensemble Subspace KNN, and Fine KNN. The experimental evaluation comprises 7 and 8 cross-validation folds. On 7 folds Ensemble Subspace KNN provides an accuracy of 97.33%, and on 8 folds Fine KNN provides the best accuracy of 97.92%.

@article{Amin2025,
  title     = {Tree Seed Algorithm-Based Optimized Deep Features Selection for Glaucoma Disease Classification},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {3},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Sherif Tawfik Amin},
  doi       = {10.14569/IJACSA.2025.0160359},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160359}
}