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 Conference Paper Faculty Publications- Conference Papers 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 James, Maria; Ramamurthy, B.; Saravanan, K.N. Title A name given to the resource DeepRetina: Transformer-Enhanced EfficientNet for Retinal Disease Classification 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 2026 6th International Conference on Advances in Electrical, Computing, Communications and Sustainable Technologies, ICAECT 2026; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICAECT68478.2026.11426056" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICAECT68478.2026.11426056</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105035740290?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105035740290?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 James M., Christ (Deemed to be University), Department of Computer Science, Bangalore, India; Ramamurthy B., Christ (Deemed to be University), Department of Computer Science, Bangalore, India; Saravanan K.N., Christ (Deemed to be University), Department of Computer Science, Bangalore, India Description An account of the resource Retinal diseases are a major cause of visual impairment in India, which requires precise and automated diagnosis tools.This paper, introduce a two-phase deep learning architecture for classifying five common retinal ailments: Glaucoma, Normal Fundus, Pathological Myopia, Hypertensive Retinopathy, and Cataract. A Swin Transformer (Swin-T) was fine-tuned on augmented retinal fundus images in the first phase to extract domain-adapted feature representations. The transformer utilize such embeddings for learning a regularized EfficientNet-inspired classifier in the second phase, with mixup augmentation and label smoothing for improving generalizability. Comprehensive experiments conducted on a carefully curated dataset of 643 test images validate that our method attains a test accuracy of 93.93%, with high precision as well as recall across all categories. The suggested pipeline strikes a suitable balance between feature abundance with transformer-based adaptation and resilient classification with EfficientNet, providing a viable tool for automated diagnosis of retinal ailments in practical clinical scenarios. 2026 IEEE. Subject The topic of the resource EfficientNet; Glaucoma; Hypertensive Retinopathy and Cataract; Normal Fundus; Pathological Myopia; Swin Transformer Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833157322-5; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online