Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Singh, Kuljeet; Verma, Vivek; P J, Alphine; Alapatt, Bosco Paul; Bhushan, Megha; Galindo, JosA. Title A name given to the resource DMRD-Net: Dual modality retinal diagnostic network with few shot episodic learning and XAI interpretability Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Information Sciences;Volume;751;Issue;;Article No.;123578; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.ins.2026.123578" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.ins.2026.123578</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037724726?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037724726?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Singh K., Department of Computer Science, Christ University, Bengaluru, 560029, India; Verma V., Department of Computer Science, Christ University, Bengaluru, 560029, India; P J A., Department of Computer Science, Christ University, Bengaluru, 560029, India; Alapatt B.P., Department of Computer Science, Christ University, Bengaluru, 560029, India; Bhushan M., Department of Computer Languages and Systems, University of Seville, Sevilla, Spain; Galindo J.A., Department of Computer Languages and Systems, University of Seville, Sevilla, Spain Description An account of the resource 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. 2026 The Author(s) Subject The topic of the resource Deep learning; Dual-modality diagnosis; Explainable AI; Few-shot learning; Optical coherence tomography; Retinal disease classification Publisher An entity responsible for making the resource available Elsevier Inc. Relation A related resource ISSN: 200255; CODEN: ISIJB Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Hybrid Gold Open Access Format The file format, physical medium, or dimensions of the resource online