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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Neisa Hibatillah Alif
Institut Teknologi Sepuluh Nopember
Computer Science & IT Engineering

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Lightweight CNN Feature Extraction and PSO-Weighted Ensemble for Retinal OCT Classification Neisa Hibatillah Alif; Anggun Dwi Rizkika; Feiticeira Zulkarnaen; Nanik Suciati
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): Juni 2026 (in progress)
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.7451

Abstract

Retinal diseases are a primary cause of visual impairment, requiring accurate and efficient automated classification. This study proposes a multi-class classification approach for detecting retinal disease from Optical Coherence Tomography (OCT) images using the OCT-C8 dataset, which consists of 24,000 images across eight balanced classes. Pretrained lightweight convolutional neural networks (EfficientNet-B0, ShuffleNetV2, and RegNetY-400MF) are used as feature extractors to leverage transfer learning while reducing computational cost. Instead of end-to-end deep learning, extracted features are classified using traditional machine learning models (KNN, RF, SVM, and MLP), which are more efficient for moderate-sized datasets. To improve robustness, a weighted ensemble is applied, where classifier contributions are optimized using Particle Swarm Optimization (PSO). Experimental results show that the proposed method achieves a classification accuracy of 97.35%, outperforming conventional hard and soft voting methods, while maintaining a balance between computational efficiency and performance with potential for practical deployment.
Co-Authors Anggun Dwi Rizkika Feiticeira Zulkarnaen Nanik Suciati