{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T05:07:47Z","timestamp":1776834467466,"version":"3.51.2"},"reference-count":30,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T00:00:00Z","timestamp":1776816000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Science and ICT (MSIT), Korea","award":["IITP-2026-RS-2024-00437718"],"award-info":[{"award-number":["IITP-2026-RS-2024-00437718"]}]},{"name":"Ministry of Science and ICT (MSIT), Korea","award":["IITP-2023-RS-2023-00256629"],"award-info":[{"award-number":["IITP-2023-RS-2023-00256629"]}]},{"name":"National Research Foundation of Korea (NRF) grant funded by the Korea government","award":["RS-2023-00208397"],"award-info":[{"award-number":["RS-2023-00208397"]}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Vet. Sci."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Feline ocular diseases can cause irreversible vision loss if they are not detected early. However, early diagnosis is often difficult. This is due to limited access to veterinary ophthalmology services and the challenge of distinguishing between visually similar eye conditions. Illumination changes, glare, and strong visual similarity among diseases substantially limit the performance of conventional RGB-based classification models. This paper proposes the Color Space Fusion Network (CSF-Net), an attention-guided color interaction framework for robust feline ocular disease classification in real-world environments.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>CSF-Net decomposes images into complementary color spaces (RGB, HSV, and YCbCr), learns independent feature representations, and applies a self-attention mechanism to emphasize disease-relevant visual cues while reducing sensitivity to environmental variations. The proposed method is evaluated on a large-scale dataset of 16,480 feline ocular images and compared with representative deep learning models, including ResNet50, EfficientNet, and ViT\/16-b.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>CSF-Net achieves superior performance, reaching an accuracy of 83.05% and achieved a mean fold-wise macro-AUC of 0.9690 across five cross-validation folds. Ablation and class-wise analyses confirm the effectiveness of self-attention in leveraging multi-color representations, while also revealing limitations for eyelid-centered conditions such as blepharitis. A mobile-based preliminary screening application, PurrfectEyes, is further presented to demonstrate practical applicability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>Overall, this work introduces an attention-guided multi-color framework that improves robustness and interpretability for AI-assisted feline ocular disease screening in real-world settings.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fvets.2026.1826139","type":"journal-article","created":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T04:32:59Z","timestamp":1776832379000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["CSF-net: a color space fusion network with self-attention-driven feature learning for feline ocular diseases classification"],"prefix":"10.3389","volume":"13","author":[{"given":"Dhivyaa","family":"S. P.","sequence":"first","affiliation":[{"name":"Department of Artificial Intelligence Convergence, Chonnam National University","place":["Gwangju, Republic of Korea"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Myungeun","family":"Lee","sequence":"additional","affiliation":[{"name":"Hyper-Wide Federated Medical AI Research Center, Chonnam National University","place":["Gwangju, Republic of Korea"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hyung-Jeong","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence Convergence, Chonnam National University","place":["Gwangju, Republic of Korea"]},{"name":"Hyper-Wide Federated Medical AI Research Center, Chonnam National University","place":["Gwangju, Republic of Korea"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,4,22]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"1098612X231151478","DOI":"10.1177\/1098612X231151478","article-title":"Prevalence of feline ophthalmic disorders in South Korea: a retrospective study (2009\u20132021)","volume":"25","author":"Park","year":"2023","journal-title":"J Feline Med Surg"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"235","DOI":"10.17555\/jvc.2015.06.32.3.235","article-title":"Epidemiological and clinical features of canine ophthalmic diseases in Seoul from 2009 to 2013","volume":"32","author":"Kim","year":"2015","journal-title":"J Vet Clin"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"105","DOI":"10.11254\/jscvo.21.105","article-title":"Diseases and surgery of the canine cornea and sclera","volume":"21","author":"Gelatt","year":"2002","journal-title":"Anim Eye Res"},{"key":"ref4","first-page":"1","article-title":"Computer-aided diagnosis for lung lesion in companion animals from X-ray images using deep learning techniques","author":"Arsomngern","year":"2019"},{"key":"ref5","first-page":"381","article-title":"AI in veterinary ophthalmology: canine eye diseases diagnosis with image segmentation and large language models","author":"Buric","year":"2024"},{"key":"ref6","volume-title":"Application of Computer vision Methods in Veterinary Ophthalmology (Doctoral Dissertation, Sveu\u010dili\u0161te u Rijeci, Sveu\u010dili\u0161te u Rijeci, Fakultet Informatike i Digitalnih Tehnologija)","author":"Buri\u0107","year":""},{"key":"ref7","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1177\/1098612X211061049","article-title":"Corneal surgery in the cat: diseases, considerations and techniques","volume":"24","author":"Telle","year":"2022","journal-title":"J Feline Med Surg"},{"key":"ref8","year":""},{"key":"ref9","year":""},{"key":"ref10","year":""},{"key":"ref11","year":""},{"key":"ref12","year":""},{"key":"ref13","doi-asserted-by":"publisher","first-page":"1728","DOI":"10.2337\/dci23-0032","article-title":"Artificial intelligence and diabetic retinopathy: AI framework, prospective studies, head-to-head validation, and cost-effectiveness","volume":"46","author":"Rajesh","year":"2023","journal-title":"Diabetes Care"},{"key":"ref14","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1186\/s12886-024-03381-1","article-title":"Artificial intelligence in age-related macular degeneration: state of the art and recent updates","volume":"24","author":"Crincoli","year":"2024","journal-title":"BMC Ophthalmol"},{"key":"ref15","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1167\/tvst.10.7.9","article-title":"Classifying retinal degeneration in histological sections using deep learning","volume":"10","author":"Al Mouiee","year":"2021","journal-title":"Transl Vis Sci Technol"},{"key":"ref16","year":""},{"key":"ref17","doi-asserted-by":"publisher","first-page":"6000","DOI":"10.5555\/3295222.3295349","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv Neural Inf Process Syst"},{"key":"ref18","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1007\/s10462-025-11198-7","article-title":"A comprehensive survey of loss functions and metrics in deep learning","volume":"58","author":"Terven","year":"2025","journal-title":"Artif Intell Rev"},{"key":"ref19","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"ref20","first-page":"6105","article-title":"Efficientnet: rethinking model scaling for convolutional neural networks","author":"Tan","year":"2019"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2010.11929","article-title":"An image is worth 16x16 words: transformers for image recognition at scale","author":"Dosovitskiy","year":"2020","journal-title":"arXiv"},{"key":"ref22","volume-title":"MobileNetV3. 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