Retinal Diseases Classification From OCT Images Using Pretrained Dual-Encoder Architecture

Retinal diseases, such as age-related macular degeneration (AMD), diabetic retinopathy (DR), and glaucoma, are leading causes of irreversible vision loss, making early and accurate diagnosis essential for effective treatment. Optical coherence tomography (OCT) provides high-resolution cross-sectional retinal images that support disease assessment; however, many challenges still remain due to noise and artifacts in images or complex retinal structures. In this study, we propose a dual-encoder framework for retinal disease classification from OCT B-scan images by jointly leveraging two pretrained foundation models: RETFound and MIRAGE. Following standardized preprocessing and resampling, high-quality features extracted from the encoders are combined for the final classification tasks. To mitigate overfitting on limited medical data, the RETFound encoder is frozen during training to preserve general visual features, whereas the MIRAGE encoder is fine-tuned to adapt to specific classification objectives. Extensive experiments conducted on seven public OCT benchmark datasets demonstrate that the proposed method outperforms single-encoder baselines on the majority of benchmarks. The framework achieved an average balanced accuracy (BAcc) of 89.8%, an F1-score of 90.7%, and a Matthews Correlation Coefficient (MCC) of 83.9%. These results confirm the effectiveness of combining complementary pretrained encoders for robust and generalizable retinal disease classification in clinical settings.