Enhancing Optical Coherence Tomography Image Classification Via Swarm Optimization-Based Feature Selection and Machine Learning Models

Zaid Al-Jubouri · Sarah Saadoon Jasim

Optical Coherence Tomography (OCT) greatly facilitates the diagnosis of retinal diseases. However, traditional models based on Convolutional Neural Networks (CNNs) suffer from challenges, most notably high computational cost, sensitivity to noise, and data imbalance. This study aims to compare three hybrid deep learning frameworks, all of which rely on feature extraction using a pre-trained CNN model and then selecting the most important features using intelligent swarm algorithms: the Dolphin Swarm Optimization (DSO), the Particle Swarm Optimization (PSO), and the Ant Swarm Optimization (ACO). The selected features were evaluated using four classifiers: SVM, random forest, XGBoost, and k-NN. Experiments were conducted on a standard dataset from the University of California, San Diego (UCSD) and a local dataset. The comparison results showed that the hybrid framework, which combines the dolphin swarm algorithm and SVM, outperformed the other combinations, achieving a classification accuracy of 93% on local data and 95% on standard data, while also outperforming them in terms of accuracy and computational efficiency.