{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T07:04:14Z","timestamp":1779865454083,"version":"3.53.1"},"reference-count":49,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Biomedical Signal Processing and Control"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.bspc.2026.110567","type":"journal-article","created":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T20:15:13Z","timestamp":1778184913000},"page":"110567","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PA","title":["VITAL-DR: Vision transformer adaptive learning for diabetes retinopathy (DR) detection and classification from fundus image"],"prefix":"10.1016","volume":"123","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5105-0188","authenticated-orcid":false,"given":"Usharani","family":"Thirunavukkarasu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5059-7269","authenticated-orcid":false,"given":"Rakesh Kumar","family":"Mahendran","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lavanya","family":"M","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.bspc.2026.110567_b0005","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1186\/s12933-024-02367-z","article-title":"Artificial intelligence-based classification of cardiac autonomic neuropathy from retinal fundus images in patients with diabetes: The Silesia Diabetes Heart Study","volume":"23","author":"Nabrdalik","year":"2024","journal-title":"Cardiovasc. Diabetol."},{"issue":"19","key":"10.1016\/j.bspc.2026.110567_b0010","doi-asserted-by":"crossref","first-page":"e7032","DOI":"10.1002\/cpe.7032","article-title":"Detection of diabetic retinopathy and related retinal disorders using fundus images based on deep learning and image processing techniques: A comprehensive review","volume":"34","author":"Lalithadevi","year":"2022","journal-title":"Concurrency Comput. Pract. Exper."},{"issue":"3","key":"10.1016\/j.bspc.2026.110567_b0015","article-title":"Understanding the clinical relationship between diabetic retinopathy, nephropathy, and neuropathy: A comprehensive review","volume":"16","author":"Kulkarni","year":"2024","journal-title":"Cureus"},{"key":"10.1016\/j.bspc.2026.110567_b0020","doi-asserted-by":"crossref","unstructured":"Mushtaq, G., & Siddiqui, F. (2021, February). Detection of diabetic retinopathy using deep learning methodology. In IOP conference series: materials science and engineering (Vol. 1070, No. 1, p. 012049). IOP Publishing.","DOI":"10.1088\/1757-899X\/1070\/1\/012049"},{"issue":"24","key":"10.1016\/j.bspc.2026.110567_b0025","doi-asserted-by":"crossref","first-page":"11970","DOI":"10.3390\/app112411970","article-title":"Diabetic retinopathy improved detection using deep learning","volume":"11","author":"Ayala","year":"2021","journal-title":"Appl. Sci."},{"issue":"Supp01","key":"10.1016\/j.bspc.2026.110567_b0030","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1142\/S0218488521400031","article-title":"Regression model-based feature filtering for improving hemorrhage detection accuracy in diabetic retinopathy treatment","volume":"29","author":"Krishnamoorthy","year":"2021","journal-title":"Int. J. Uncertainty Fuzziness Knowledge Based Syst."},{"issue":"5","key":"10.1016\/j.bspc.2026.110567_b0035","doi-asserted-by":"crossref","first-page":"1376","DOI":"10.1049\/cit2.70039","article-title":"VSMI 2 VSMI^2\u2010PANet: Versatile scale\u2010malleable image integration and patch wise attention network with transformer for lung tumour segmentation using multi\u2010modal imaging techniques","volume":"10","author":"Alqahtani","year":"2025","journal-title":"CAAI Trans. Intell. Technol."},{"key":"10.1016\/j.bspc.2026.110567_b0040","first-page":"7619","article-title":"Recent automated hard exudates detection systems in diabetic retinopathy","author":"Gorde","year":"2021","journal-title":"NVEO-Natural Volatiles & Essential Oils J.| NVEO"},{"issue":"3","key":"10.1016\/j.bspc.2026.110567_b0045","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1016\/j.jfo.2020.08.009","article-title":"Survey on recent developments in automatic detection of diabetic retinopathy","volume":"44","author":"Bilal","year":"2021","journal-title":"J. Fran\u00e7ais D'ophtalmologie"},{"issue":"12","key":"10.1016\/j.bspc.2026.110567_b0050","article-title":"Identification of microaneurysms and exudates for early detection of diabetic retinopathy","volume":"14","author":"Devi","year":"2023","journal-title":"Int. J. Adv. Comp. Sci. Appl."},{"key":"10.1016\/j.bspc.2026.110567_b0055","doi-asserted-by":"crossref","first-page":"2515","DOI":"10.2147\/OPTH.S414603","article-title":"Comparison of color fundus photography and multicolor fundus imaging for detection of lesions in diabetic retinopathy and retinal vein occlusion","author":"Castro","year":"2023","journal-title":"Clin. Ophthalmol."},{"key":"10.1016\/j.bspc.2026.110567_b0060","doi-asserted-by":"crossref","unstructured":"Bannigidad, P., & Deshpande, A. (2021). The Fusion of Features for Detection of Cotton Wool Spots in Digital Fundus Images. In Proceedings of the 12th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2020) 12 (pp. 530-538). Springer International Publishing.","DOI":"10.1007\/978-3-030-73689-7_51"},{"key":"10.1016\/j.bspc.2026.110567_b0065","first-page":"1","article-title":"Automated detection of severe diabetic retinopathy using deep learning method","author":"Zhang","year":"2022","journal-title":"Graefes Arch. Clin. Exp. Ophthalmol."},{"issue":"16","key":"10.1016\/j.bspc.2026.110567_b0070","doi-asserted-by":"crossref","first-page":"8326","DOI":"10.3390\/app12168326","article-title":"A computer-aided diagnostic system for diabetic retinopathy based on local and global extracted features","volume":"12","author":"Haggag","year":"2022","journal-title":"Appl. Sci."},{"issue":"25","key":"10.1016\/j.bspc.2026.110567_b0075","doi-asserted-by":"crossref","first-page":"39327","DOI":"10.1007\/s11042-023-15045-1","article-title":"Detection and classification of red lesions from retinal images for diabetic retinopathy detection using deep learning models","volume":"82","author":"Saranya","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"10.1016\/j.bspc.2026.110567_b0080","doi-asserted-by":"crossref","unstructured":"Mohamed, E., Abd Elmohsen, M., & Basha, T. (2021, November). Improved automatic grading of diabetic retinopathy using deep learning and principal component analysis. In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 3898-3901). IEEE.","DOI":"10.1109\/EMBC46164.2021.9630919"},{"issue":"6","key":"10.1016\/j.bspc.2026.110567_b0085","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1007\/s42979-021-00833-z","article-title":"A survey on automatic diabetic retinopathy screening","volume":"2","author":"Nage","year":"2021","journal-title":"SN Comput. Sci."},{"issue":"2","key":"10.1016\/j.bspc.2026.110567_b0090","doi-asserted-by":"crossref","first-page":"195","DOI":"10.2174\/1872212114666200109103922","article-title":"Two-tier grading system for npdr severities of diabetic retinopathy in retinal fundus images","volume":"15","author":"Bhardwaj","year":"2021","journal-title":"Recent Pat. Eng."},{"key":"10.1016\/j.bspc.2026.110567_b0095","series-title":"Augmented Intelligence in Healthcare: A Pragmatic and Integrated Analysis","first-page":"87","article-title":"Analysis of diabetic retinopathy detection techniques using CNN models","author":"Prabhavathy","year":"2022"},{"key":"10.1016\/j.bspc.2026.110567_b0100","article-title":"Dataset from fundus images for the study of diabetic retinopathy","volume":"36","author":"Ben\u00edtez","year":"2021","journal-title":"Data Brief"},{"key":"10.1016\/j.bspc.2026.110567_b0105","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1109\/JTEHM.2023.3282104","article-title":"A hybrid convolutional neural network model for automatic diabetic retinopathy classification from fundus images","volume":"11","author":"Ali","year":"2023","journal-title":"IEEE J. Trans. Eng. Health Med."},{"issue":"6","key":"10.1016\/j.bspc.2026.110567_b0110","doi-asserted-by":"crossref","first-page":"1789","DOI":"10.1109\/TPDS.2023.3264473","article-title":"DRFL: Federated learning in diabetic retinopathy grading using fundus images","volume":"34","author":"Mohan","year":"2023","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"issue":"11","key":"10.1016\/j.bspc.2026.110567_b0115","doi-asserted-by":"crossref","first-page":"11407","DOI":"10.1109\/TCYB.2021.3062638","article-title":"Robust collaborative learning of patch-level and image-level annotations for diabetic retinopathy grading from fundus image","volume":"52","author":"Yang","year":"2021","journal-title":"IEEE Trans. Cybern."},{"key":"10.1016\/j.bspc.2026.110567_b0120","article-title":"A cross-lesion attention network for accurate diabetic retinopathy grading with fundus images","author":"Liu","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"5","key":"10.1016\/j.bspc.2026.110567_b0125","doi-asserted-by":"crossref","first-page":"2216","DOI":"10.1109\/JBHI.2021.3119519","article-title":"Joint learning of multi-level tasks for diabetic retinopathy grading on low-resolution fundus images","volume":"26","author":"Wang","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.bspc.2026.110567_b0130","doi-asserted-by":"crossref","first-page":"108276","DOI":"10.1109\/ACCESS.2021.3101142","article-title":"Diabetic retinopathy diagnosis from fundus images using stacked generalization of deep models","volume":"9","author":"Kaushik","year":"2021","journal-title":"IEEE Access"},{"key":"10.1016\/j.bspc.2026.110567_b0135","doi-asserted-by":"crossref","first-page":"61408","DOI":"10.1109\/ACCESS.2021.3074422","article-title":"Diabetic retinopathy detection using VGG-NIN a deep learning architecture","volume":"9","author":"Khan","year":"2021","journal-title":"IEEE Access"},{"key":"10.1016\/j.bspc.2026.110567_b0140","doi-asserted-by":"crossref","first-page":"23544","DOI":"10.1109\/ACCESS.2021.3056186","article-title":"Diabetic retinopathy detection and classification using mixed models for a disease grading database","volume":"9","author":"Bilal","year":"2021","journal-title":"IEEE Access"},{"key":"10.1016\/j.bspc.2026.110567_b0145","doi-asserted-by":"crossref","first-page":"55522","DOI":"10.1109\/ACCESS.2022.3177651","article-title":"Texture attention network for diabetic retinopathy classification","volume":"10","author":"Alahmadi","year":"2022","journal-title":"IEEE Access"},{"issue":"4","key":"10.1016\/j.bspc.2026.110567_b0150","article-title":"Multi-scale attention-based mechanism in gradient boosting convolutional neural network for diabetic retinopathy grade classification","volume":"15","author":"Srinivasan","year":"2022","journal-title":"Int. J. Intelligent Eng. Sys."},{"key":"10.1016\/j.bspc.2026.110567_b0155","doi-asserted-by":"crossref","first-page":"160552","DOI":"10.1109\/ACCESS.2021.3131630","article-title":"A novel diabetic retinopathy detection approach based on deep symmetric convolutional neural network","volume":"9","author":"Liu","year":"2021","journal-title":"IEEE Access"},{"issue":"8","key":"10.1016\/j.bspc.2026.110567_b0160","doi-asserted-by":"crossref","first-page":"11691","DOI":"10.1007\/s11042-020-10238-4","article-title":"Diabetic retinopathy detection and stage classification in eye fundus images using active deep learning","volume":"80","author":"Qureshi","year":"2021","journal-title":"Multimed. Tools Appl."},{"issue":"1","key":"10.1016\/j.bspc.2026.110567_b0165","doi-asserted-by":"crossref","first-page":"1897","DOI":"10.1038\/s41598-021-81539-3","article-title":"Early detection of diabetic retinopathy based on deep learning and ultra-wide-field fundus images","volume":"11","author":"Oh","year":"2021","journal-title":"Sci. Rep."},{"issue":"11","key":"10.1016\/j.bspc.2026.110567_b0170","doi-asserted-by":"crossref","first-page":"3704","DOI":"10.3390\/s21113704","article-title":"Diabetic retinopathy fundus image classification and lesions localization system using deep learning","volume":"21","author":"Alyoubi","year":"2021","journal-title":"Sensors"},{"issue":"7","key":"10.1016\/j.bspc.2026.110567_b0175","doi-asserted-by":"crossref","first-page":"1607","DOI":"10.3390\/diagnostics12071607","article-title":"Diabetic retinopathy detection from fundus images of the eye using hybrid deep learning features","volume":"12","author":"Butt","year":"2022","journal-title":"Diagnostics"},{"key":"10.1016\/j.bspc.2026.110567_b0180","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2021.102600","article-title":"Deep learning architecture based on segmented fundus image features for classification of diabetic retinopathy","volume":"68","author":"Das","year":"2021","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.bspc.2026.110567_b0185","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.103810","article-title":"Diabetic retinopathy classification using a novel DAG network based on multi-feature of fundus images","volume":"77","author":"Fang","year":"2022","journal-title":"Biomed. Signal Process. Control"},{"issue":"6","key":"10.1016\/j.bspc.2026.110567_b0190","doi-asserted-by":"crossref","DOI":"10.1117\/1.JEI.32.6.063011","article-title":"Diabetic retinopathy classification using hybrid optimized deep-learning network model in fundus images","volume":"32","author":"Bapatla","year":"2023","journal-title":"J. Electron. Imaging"},{"issue":"3","key":"10.1016\/j.bspc.2026.110567_b0195","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1007\/s13246-022-01143-1","article-title":"Retinal fundus image classification for diabetic retinopathy using SVM predictions","volume":"45","author":"Hardas","year":"2022","journal-title":"Phys. Eng. Sci. Med."},{"key":"10.1016\/j.bspc.2026.110567_b0200","doi-asserted-by":"crossref","DOI":"10.3389\/fphys.2022.961386","article-title":"FunSwin: A deep learning method to analysis diabetic retinopathy grade and macular edema risk based on fundus images","volume":"13","author":"Yao","year":"2022","journal-title":"Front. Physiol."},{"key":"10.1016\/j.bspc.2026.110567_b0205","doi-asserted-by":"crossref","DOI":"10.1016\/j.indcrop.2024.120241","article-title":"Vision transformers for cotton boll segmentation: hyperparameters optimization and comparison with convolutional neural networks","volume":"223","author":"Singh","year":"2025","journal-title":"Ind. Crop. Prod."},{"issue":"5","key":"10.1016\/j.bspc.2026.110567_b0210","doi-asserted-by":"crossref","first-page":"1248","DOI":"10.1109\/TAI.2024.3521870","article-title":"Improved supervised machine learning for predicting auto insurance purchase patterns","volume":"6","author":"Nachaoui","year":"2024","journal-title":"IEEE Trans. Artif. Intell."},{"key":"10.1016\/j.bspc.2026.110567_b0215","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2025.108110","article-title":"Pixel attention meets M-shaped networks: A cutting-edge AI solution for diabetic retinopathy classification and stroke risk prediction","volume":"110","author":"Alsheikhy","year":"2025","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.bspc.2026.110567_b0220","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2024.107035","article-title":"Deep neural network model for diagnosing diabetic retinopathy detection: An efficient mechanism for diabetic management","volume":"100","author":"Muthusamy","year":"2025","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.bspc.2026.110567_b0225","doi-asserted-by":"crossref","DOI":"10.1007\/s10462-024-10806-2","article-title":"Deep learning model using classification for diabetic retinopathy detection: an overview","volume":"57","author":"Muthusamy","year":"2024","journal-title":"Artif. Intell. Rev."},{"key":"10.1016\/j.bspc.2026.110567_b0230","article-title":"Automatic screening for diabetic retinopathy in interracial fundus images using artificial intelligence","volume":"3","author":"Katada","year":"2020","journal-title":"Intelligence-Based Med."},{"key":"10.1016\/j.bspc.2026.110567_b0235","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.128116","article-title":"Enhanced multi-grade diabetic retinopathy detection and classification via ensembled deep learning model from retinal fundus images","volume":"285","author":"Hariobulesu","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.bspc.2026.110567_b0240","series-title":"Computers in Biology and Medicine","article-title":"Detection and classification of diabetic retinopathy in retinal fundus images using deep spiking Q Network optimized with partial reinforcement optimizer","author":"Rayavel","year":"2025"},{"key":"10.1016\/j.bspc.2026.110567_b0245","doi-asserted-by":"crossref","DOI":"10.1016\/j.is.2026.102696","article-title":"HEAL-LLMF: A hierarchical explainable AI framework using LLM Fusion for multi-modal and personalized healthcare decision making","author":"Haq","year":"2026","journal-title":"Inf. Syst."}],"container-title":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426011213?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426011213?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T06:24:50Z","timestamp":1779863090000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1746809426011213"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":49,"alternative-id":["S1746809426011213"],"URL":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110567","relation":{},"ISSN":["1746-8094"],"issn-type":[{"value":"1746-8094","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"VITAL-DR: Vision transformer adaptive learning for diabetes retinopathy (DR) detection and classification from fundus image","name":"articletitle","label":"Article Title"},{"value":"Biomedical Signal Processing and Control","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110567","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"110567"}}