ItemArticle

DMRD-Net: Dual modality retinal diagnostic network with few shot episodic learning and XAI interpretability

Title
DMRD-Net: Dual modality retinal diagnostic network with few shot episodic learning and XAI interpretability
Creator
Singh, Kuljeet; Verma, Vivek; P J, Alphine; Alapatt, Bosco Paul; Bhushan, Megha; Galindo, JosA.
Description
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)
Source
Information Sciences;Volume;751;Issue;;Article No.;123578;
Date
01-01-2026
Publisher
Elsevier Inc.
Subject
Deep learning; Dual-modality diagnosis; Explainable AI; Few-shot learning; Optical coherence tomography; Retinal disease classification
Coverage
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
Rights
All Open Access; Hybrid Gold Open Access
Relation
ISSN: 200255; CODEN: ISIJB
Format
online
Language
English
Type
Article

Citation

Singh, Kuljeet; Verma, Vivek; P J, Alphine; Alapatt, Bosco Paul; Bhushan, Megha; Galindo, JosA., “DMRD-Net: Dual modality retinal diagnostic network with few shot episodic learning and XAI interpretability,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/22317.

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