DMRD-Net: Dual modality retinal diagnostic network with few shot episodic learning and XAI interpretability
Early diagnosis of retinal pathologies is critical for preventing irreversible blindness, particularly in rare conditions with limited labeled medical data. Traditional diagnostics employ a single imaging modality, limiting the identification of heterogeneous anomalies in the retina. DMRD- Net, a di...
| Autores: | , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2026 |
| País: | España |
| Institución: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:dnet:idus________::9ecb609dc8ef4aae98cc826510bb6ec9 |
| Acceso en línea: | https://hdl.handle.net/11441/186307 https://doi.org/10.1016/j.ins.2026.123578 |
| Access Level: | acceso abierto |
| Palabra clave: | Deep learning Dual-modality diagnosis Few-shot learning Retinal disease classification Optical coherence tomography Explainable AI |
| Sumario: | Early diagnosis of retinal pathologies is critical for preventing irreversible blindness, particularly in rare conditions with limited labeled medical data. Traditional diagnostics employ a single imaging modality, limiting the identification of heterogeneous anomalies in the retina. DMRD- Net, a diagnostic system is presented that integrates spectral-domain optical coherence tomography with fundus photographs, utilizing two parallel branches of a neural network, that is EfficientNet-B0 encoders and few-shot episodic meta-learning module based on Prototypical Networks, that merge their outputs to enhance the precision of diagnosis. Supervised learning methodologies are employed to identify common retinal diseases, followed by the application of meta-learning technique, referred to as Prototypical Networks, to aggregate a limited set of data for the study of rare diseases. To support clinical confidence and improve transparency, explainable artificial intelligence is utilized to facilitate decision-making by models. It facilitated the evaluation of performance on both common and rare retinal disorders. The system achieved over 96% episodic accuracy in diagnosing rare conditions, including Macular Hole, Retinitis Pigmentosa, and Stargardt Disease, in Central Serous Chorioretinopathy. The overall classification accuracy for common diseases was 96.5%. Overall, DMRD-Net is a unified, data-efficient, and interpretable multimodal diagnostic system that works well for both common and rare retinal disorders. |
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