{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T19:45:19Z","timestamp":1776887119361,"version":"3.51.2"},"reference-count":122,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/access.2025.3583647","type":"journal-article","created":{"date-parts":[[2025,6,26]],"date-time":"2025-06-26T13:44:14Z","timestamp":1750945454000},"page":"116869-116886","source":"Crossref","is-referenced-by-count":11,"title":["An Insight on the Timely Diagnosis of Diabetic Retinopathy Using Traditional and AI-Driven Approaches"],"prefix":"10.1109","volume":"13","author":[{"given":"Malaika","family":"Asif","sequence":"first","affiliation":[{"name":"Department of Computer Science, Institute of Space Technology, KICSIT Campus, Islamabad, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-6164-7437","authenticated-orcid":false,"given":"Fasih","family":"Ur Rehman","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Institute of Space Technology, KICSIT Campus, Islamabad, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-5918-6720","authenticated-orcid":false,"given":"Zoya","family":"Rashid","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Institute of Space Technology, KICSIT Campus, Islamabad, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0558-1380","authenticated-orcid":false,"given":"Altaf","family":"Hussain","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Institute of Space Technology, KICSIT Campus, Islamabad, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7227-253X","authenticated-orcid":false,"given":"Alina","family":"Mirza","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, National University of Sciences and Technology, Islamabad, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0176-8145","authenticated-orcid":false,"given":"Waqar Shahid","family":"Qureshi","sequence":"additional","affiliation":[{"name":"School of Computer Science, University of Galway, Galway, Ireland"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1152\/physrev.00063.2017"},{"issue":"18","key":"ref2","doi-asserted-by":"crossref","first-page":"3126","DOI":"10.1242\/dev.120063","article-title":"Human pancreas development","volume":"142","author":"Jennings","year":"2015","journal-title":"Development"},{"issue":"1","key":"ref3","first-page":"9091","article-title":"Association between hyperglycemia and retinopathy of prematurity: A systemic review and meta-analysis","volume-title":"Sci. Rep.","volume":"5","author":"Au","year":"2015"},{"issue":"9","key":"ref4","doi-asserted-by":"crossref","first-page":"807","DOI":"10.1016\/j.amjmed.2014.04.015","article-title":"The epidemic of the 20th century: Coronary heart disease","volume":"127","author":"Dalen","year":"2014","journal-title":"Amer. J. Med."},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1111\/joim.13261"},{"key":"ref6","volume-title":"Retinal Imaging Guide","year":"2021"},{"issue":"1","key":"ref7","doi-asserted-by":"crossref","first-page":"100","DOI":"10.3390\/diagnostics13010100","article-title":"Comprehensive review on the use of artificial intelligence in ophthalmology and future research directions","volume":"13","author":"Anton","year":"2022","journal-title":"Diagnostics"},{"issue":"1","key":"ref8","article-title":"Glaucoma retinal image detection and classification using machine learning algorithms","volume-title":"J. Phys., Conf. Ser.","volume":"2335","author":"Latha"},{"key":"ref9","article-title":"Retinal damage alters gene expression profile in lacrimal glands of rats","volume":"215","author":"Kawasaki","year":"2022","journal-title":"Experim. Eye Res."},{"issue":"1","key":"ref10","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1076\/soph.16.1.2.4220","article-title":"Diabetic retinopathy\u2013An historical review","volume":"16","author":"Wolfensberger","year":"2001","journal-title":"Seminars Ophthalmology"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-97-5231-7_15"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref13","volume-title":"R\u00e9tinopathie Diab\u00e9tique","author":"Massin","year":"2025"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.3390\/fi16090308"},{"issue":"5","key":"ref15","first-page":"1716","article-title":"Detection of microaneurysms using multi-scale correlation coefficients","volume":"48","author":"Srinivasan","year":"2010","journal-title":"Pattern Recognit."},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2022.9960"},{"issue":"1","key":"ref17","first-page":"72","article-title":"Impact of parental perception on pediatric vision care in rural and urban areas of district rahim yar khan","volume":"3","author":"Naeem","year":"2025","journal-title":"Insights-Journal Life Social Sci."},{"issue":"10","key":"ref18","first-page":"36","article-title":"Spatially resolved association of structural biomarkers on retinal imaging with disease progression in nonexudative age-related macular degeneration","volume":"61","author":"Sadda","year":"2020","journal-title":"Investigative Ophthalmology Vis. Sci."},{"issue":"1","key":"ref19","first-page":"27","article-title":"Detecting diabetic retinopathy using an artificial intelligence-based software platform: A pilot study","volume":"108","author":"Nevska","year":"2024","journal-title":"J. Ophthalmology (Ukraine)"},{"key":"ref20","volume-title":"Retinopat\u00cda Diab\u00c9tica","author":"Ophthalmology","year":"2025"},{"issue":"4","key":"ref21","first-page":"47","article-title":"Detection and classification of eye disease using deep learning algorithms","volume":"10","author":"Laxman Deokate","year":"2025","journal-title":"J. Inf. Syst. Eng. Manage."},{"issue":"9","key":"ref22","doi-asserted-by":"crossref","first-page":"1373","DOI":"10.18240\/ijo.2023.09.03","article-title":"Guidelines for the application of artificial intelligence in the diagnosis of anterior segment diseases (2023)","volume":"16","author":"Shao","year":"2023","journal-title":"Int. J. Ophthalmology"},{"key":"ref23","article-title":"Deep learning algorithms for detection of diabetic retinopathy in retinal fundus photographs","volume":"162","author":"Karray","year":"2020","journal-title":"Diabetes Res. Clin. Pract."},{"issue":"3","key":"ref24","first-page":"197","article-title":"Artificial intelligence in ophthalmology: Accuracy, challenges, and clinical application","volume":"8","author":"Tan","year":"2019","journal-title":"Asia\u2013Pacific J. Ophthalmology"},{"issue":"1","key":"ref25","article-title":"Detection of retinal nerve fiber layer defects on retinal fundus images using Gabor filtering","volume":"15","author":"Hatanaka","year":"2007","journal-title":"SPIE Proc."},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/s42979-023-01734-z"},{"key":"ref27","article-title":"Impact of artificial intelligence assessment of diabetic retinopathy screening on referral uptake in Rwanda","volume":"45","author":"Nkurunziza","year":"2022","journal-title":"EClinicalMedicine"},{"issue":"9","key":"ref28","doi-asserted-by":"crossref","first-page":"1342","DOI":"10.1038\/s41591-018-0107-6","article-title":"Clinically applicable deep learning for diagnosis and referral in retinal disease","volume":"24","author":"De Fauw","year":"2018","journal-title":"Nature Med."},{"issue":"7","key":"ref29","first-page":"1004","article-title":"Artificial intelligence for retinal diseases","volume":"67","author":"Sivaprasad","year":"2024","journal-title":"Indian J. Ophthalmology"},{"issue":"2","key":"ref30","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1038\/eye.2012.287","article-title":"The royal college of Ophthalmologists\u2019 clinical guidelines for diabetic retinopathy: A summary","volume":"27","author":"Ghanchi","year":"2013","journal-title":"Eye"},{"key":"ref31","doi-asserted-by":"crossref","DOI":"10.1016\/j.health.2023.100140","article-title":"A deep neural network and machine learning approach for retinal fundus image classification","volume":"3","author":"Thanki","year":"2023","journal-title":"Healthcare Analytics"},{"issue":"1","key":"ref32","doi-asserted-by":"crossref","DOI":"10.1016\/j.jik.2023.100333","article-title":"A systematic literature review of artificial intelligence in the healthcare sector: Benefits, challenges, methodologies, and functionalities","volume":"8","author":"Ali","year":"2023","journal-title":"J. Innov. Knowl."},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICEES.2019.8719242"},{"issue":"2","key":"ref34","first-page":"145","article-title":"Analysis of early diabetic retinopathy by computer processing of fundus images\u2013A preliminary study","volume":"4","author":"Nagasaka","year":"1987","journal-title":"Diabetic Med."},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-021-06770-5"},{"issue":"1","key":"ref36","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.preteyeres.2005.07.001","article-title":"Retinal image analysis: Concepts, applications and potential","volume":"25","author":"Patton","year":"2006","journal-title":"Prog. Retinal Eye Res."},{"issue":"2","key":"ref37","doi-asserted-by":"crossref","first-page":"212","DOI":"10.3390\/healthcare11020212","article-title":"A deep learning-based framework for retinal disease classification","volume":"11","author":"Choudhary","year":"2023","journal-title":"Healthcare"},{"issue":"3","key":"ref38","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1038\/s41433-019-0566-0","article-title":"Artificial intelligence for diabetic retinopathy screening: A review","volume":"34","author":"Grzybowski","year":"2019","journal-title":"Eye"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1155\/2011\/683267"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/INTERCON.2017.8079692"},{"key":"ref41","doi-asserted-by":"crossref","first-page":"l4898","DOI":"10.1136\/bmj.l4898","article-title":"RoB 2: A revised tool for assessing risk of bias in randomised trials","author":"Sterne","year":"2019","journal-title":"BMJ"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1002\/jrsm.1411"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-publhealth-090419-102644"},{"issue":"1","key":"ref44","doi-asserted-by":"crossref","first-page":"15019","DOI":"10.1038\/nrdp.2015.19","article-title":"Type 2 diabetes mellitus","volume":"1","author":"DeFronzo","year":"2015","journal-title":"Nature Rev. Disease Primers"},{"issue":"3","key":"ref45","first-page":"477","article-title":"Hypertension and metabolic abnormalities: The metabolic syndrome","volume":"45","author":"Sowers","year":"2005","journal-title":"Hypertension"},{"issue":"4","key":"ref46","first-page":"610","article-title":"Screening for type 1 diabetes in the general population: A status report and perspective","volume-title":"Diabetes","volume":"71","author":"Sims","year":"2022"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1038\/s41572-019-0098-8"},{"issue":"6","key":"ref48","doi-asserted-by":"crossref","first-page":"2802","DOI":"10.1097\/MS9.0000000000000894","article-title":"Comprehensive review of diabetic ketoacidosis: An update","volume":"85","author":"Elendu","year":"2023","journal-title":"Ann. Med. Surgery"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1007\/s00125-005-1960-7"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1186\/1750-1172-3-17"},{"key":"ref51","first-page":"171","article-title":"Maturity-onset diabetes of the young: Clinical characteristics and diagnostic criteria","volume":"6","author":"Hattersley","year":"2013","journal-title":"Diabetes, Metabolic Syndrome Obesity, Targets Therapy"},{"issue":"3","key":"ref52","first-page":"39422","article-title":"Understanding diabetes mellitus causes, symptoms, and Complications","volume":"50","author":"Grunbaum","year":"2023","journal-title":"Biomed. J. Sci. Tech. Res."},{"key":"ref53","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2018\/3086167","article-title":"Complications of diabetes 2017","volume":"2018","author":"Papatheodorou","year":"2018","journal-title":"J. Diabetes Res."},{"key":"ref54","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2015\/268390","article-title":"Diabetic foot syndrome as a possible cardiovascular marker in diabetic patients","volume":"2015","author":"Tuttolomondo","year":"2015","journal-title":"J. Diabetes Res."},{"key":"ref55","volume-title":"The Effects of Diabetes on Your Body","author":"Team","year":"2025"},{"key":"ref56","volume-title":"Diabetic Retinopathy","author":"Institute","year":"2024"},{"key":"ref57","volume-title":"Diabetic retinopathy","author":"Associates","year":"2025"},{"issue":"46","key":"ref58","first-page":"21","article-title":"Diabetic retinopathy: Clinical findings and management","volume":"16","author":"Viswanath","year":"2024","journal-title":"Community Eye Health"},{"issue":"4","key":"ref59","doi-asserted-by":"crossref","first-page":"152","DOI":"10.3390\/bdcc6040152","article-title":"A systematic literature review on diabetic retinopathy using an artificial intelligence approach","volume":"6","author":"Bidwai","year":"2022","journal-title":"Big Data Cognit. Comput."},{"issue":"4","key":"ref60","doi-asserted-by":"crossref","first-page":"365","DOI":"10.4062\/biomolther.2020.204","article-title":"Diabetic Nephropathy\u2014A review of risk factors, progression, mechanism, and dietary management","volume":"29","author":"Natesan","year":"2021","journal-title":"Biomolecules Therapeutics"},{"key":"ref61","volume-title":"Diabetic retinopathy","author":"Shukla","year":"2025"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1186\/1559-0275-11-29"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2009.06.003"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1159\/000337156"},{"issue":"1","key":"ref65","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.ajo.2009.02.031","article-title":"Screening for diabetic retinopathy: 1 and 3 nonmydriatic 45-degree digital fundus photographs vs 7 standard early treatment diabetic retinopathy study fields","volume":"148","author":"Vujosevic","year":"2009","journal-title":"Amer. J. Ophthalmology"},{"issue":"2","key":"ref66","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1016\/S0002-9394(02)01522-2","article-title":"The sensitivity and specificity of single-field nonmydriatic monochromatic digital fundus photography with remote image interpretation for diabetic retinopathy screening: A comparison with ophthalmoscopy and standardized mydriatic color photography","volume":"134","author":"Lin","year":"2002","journal-title":"Amer. J. Ophthalmology"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1046\/j.1464-5491.2003.00969.x"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1016\/j.diabet.2007.12.007"},{"issue":"12","key":"ref69","doi-asserted-by":"crossref","first-page":"2086","DOI":"10.2337\/diacare.24.12.2086","article-title":"Two-field photography can identify patients with vision-threatening diabetic retinopathy: A screening approach in the primary care setting","volume":"24","author":"Stellingwerf","year":"2001","journal-title":"Diabetes Care"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1046\/j.1464-5491.2000.00250.x"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1177\/1932296816629491"},{"issue":"1","key":"ref72","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.preteyeres.2007.07.005","article-title":"State-of-the-art retinal optical coherence tomography","volume":"27","author":"DREXLER","year":"2008","journal-title":"Prog. Retinal Eye Res."},{"issue":"3","key":"ref73","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/j.preteyeres.2006.03.001","article-title":"Retinal assessment using optical coherence tomography","volume":"25","author":"Costa","year":"2006","journal-title":"Prog. Retinal Eye Res."},{"issue":"3","key":"ref74","doi-asserted-by":"crossref","first-page":"182","DOI":"10.3928\/1542-8877-20000501-04","article-title":"Optical coherence tomography for retinal thickness measurement in diabetic patients without clinically significant macular edema","volume":"31","author":"Schaudig","year":"2000","journal-title":"Ophthalmic Surgery, Lasers Imag. Retina"},{"issue":"6","key":"ref75","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1038\/eye.2012.53","article-title":"Observational study of subclinical diabetic macular edema","volume":"26","author":"Bressler","year":"2012","journal-title":"Eye"},{"issue":"2","key":"ref76","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.bj.2016.04.003","article-title":"Clinical applications of spectral domain optical coherence tomography in retinal diseases","volume":"39","author":"Murthy","year":"2016","journal-title":"Biomed. J."},{"issue":"4","key":"ref77","doi-asserted-by":"crossref","DOI":"10.1002\/14651858.CD008081.pub3","article-title":"Optical coherence tomography (OCT) for detection of macular oedema in patients with diabetic retinopathy","volume":"2015","author":"Virgili","year":"2015","journal-title":"Cochrane Database Systematic Rev."},{"issue":"1","key":"ref78","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.ajo.2014.09.041","article-title":"Reevaluating the definition of intraretinal microvascular abnormalities and neovascularization elsewhere in diabetic retinopathy using optical coherence tomography and fluorescein angiography","volume":"159","author":"Lee","year":"2015","journal-title":"Amer. J. Ophthalmology"},{"issue":"5","key":"ref79","doi-asserted-by":"crossref","first-page":"351","DOI":"10.4103\/0301-4738.55070","article-title":"Effective pupil dilatation with a mixture of 0.75% tropicamide and 2.5% phenylephrine: A randomized controlled trial","volume":"57","author":"Trinavarat","year":"2009","journal-title":"Indian J. Ophthalmology"},{"issue":"15","key":"ref80","doi-asserted-by":"crossref","first-page":"2582","DOI":"10.3390\/diagnostics13152582","article-title":"What is machine learning, artificial neural networks and deep learning?\u2014Examples of practical applications in medicine","volume":"13","author":"Kufel","year":"2023","journal-title":"Diagnostics"},{"key":"ref81","volume-title":"Messidor-Digital Retinopathy Database","year":"2024"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1049\/iet-cvi.2018.5263"},{"issue":"7","key":"ref83","doi-asserted-by":"crossref","first-page":"1128","DOI":"10.3390\/jpm13071128","article-title":"Performance of a support vector machine learning tool for diagnosing diabetic retinopathy in clinical practice","volume":"13","author":"Nissen","year":"2023","journal-title":"J. Personalized Med."},{"issue":"23s","key":"ref84","first-page":"824","article-title":"Diabetic retinopathy prediction using soft computing-based fuzzy-SVM integrated diabetic retinopathy prediction framework (SC-FSIDR-PF)","volume":"12","author":"Anitha","year":"2024","journal-title":"Int. J. Intell. Syst. Appl. Eng."},{"key":"ref85","volume-title":"Random Forest: A Complete Guide for Machine Learning","author":"Donges","year":"2024"},{"key":"ref86","volume-title":"Eyepacs, Aptos & Messidor Diabetic Retinopathy","year":"2020"},{"key":"ref87","volume-title":"Exploring Resnet50: An In-depth Look At the Model Architecture and Code Implementation","author":"Kundu","year":"2020"},{"issue":"5","key":"ref88","doi-asserted-by":"crossref","first-page":"567","DOI":"10.3390\/e22050567","article-title":"Machine learning based automated segmentation and hybrid feature analysis for diabetic retinopathy classification using fundus image","volume":"22","author":"Ali","year":"2020","journal-title":"Entropy"},{"issue":"1","key":"ref89","first-page":"2","article-title":"A tutorial survey of architectures, algorithms, and applications for deep learning","volume":"3","author":"Deng","year":"2014","journal-title":"APSIPA Trans. Signal Inf. Process."},{"issue":"3","key":"ref90","doi-asserted-by":"crossref","first-page":"47","DOI":"10.3390\/mti2030047","article-title":"Deep learning and medical diagnosis: A review of literature","volume":"2","author":"Bakator","year":"2018","journal-title":"Multimodal Technol. Interact."},{"issue":"6","key":"ref91","doi-asserted-by":"crossref","first-page":"749","DOI":"10.3390\/sym11060749","article-title":"Recent development on detection methods for the diagnosis of diabetic retinopathy","volume":"11","author":"Qureshi","year":"2019","journal-title":"Symmetry"},{"key":"ref92","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-65981-7_12"},{"issue":"12","key":"ref93","doi-asserted-by":"crossref","first-page":"2054","DOI":"10.3390\/molecules22122054","article-title":"Deep convolutional neural network-based early automated detection of diabetic retinopathy using fundus image","volume":"22","author":"Xu","year":"2017","journal-title":"Molecules"},{"key":"ref94","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1109\/ACCESS.2014.2325029","article-title":"Big data deep learning: Challenges and perspectives","volume":"2","author":"Chen","year":"2014","journal-title":"IEEE Access"},{"issue":"5","key":"ref95","doi-asserted-by":"crossref","first-page":"584","DOI":"10.1109\/TMI.2005.843738","article-title":"Automatic detection of red lesions in digital color fundus photographs","volume":"24","author":"Niemeijer","year":"2005","journal-title":"IEEE Trans. Med. Imag."},{"key":"ref96","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.procs.2016.07.014","article-title":"Convolutional neural networks for diabetic retinopathy","volume":"90","author":"Pratt","year":"2016","journal-title":"Proc. Comput. Sci."},{"key":"ref97","first-page":"354","article-title":"Automated staging of diabetic retinopathy using a 2D convolutional neural network","volume-title":"Proc. IEEE Int. Symp. Signal Process. Inf. Technol. (ISSPIT)","author":"Shaban"},{"key":"ref98","doi-asserted-by":"publisher","DOI":"10.1113\/jphysiol.1968.sp008455"},{"key":"ref99","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-15825-4_10"},{"key":"ref100","doi-asserted-by":"publisher","DOI":"10.5244\/C.28.6"},{"issue":"22","key":"ref101","doi-asserted-by":"crossref","first-page":"2402","DOI":"10.1001\/jama.2016.17216","article-title":"Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs","volume":"316","author":"Glshan","year":"2016","journal-title":"JAMA"},{"key":"ref102","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.media.2017.04.012","article-title":"Deep image mining for diabetic retinopathy screening","volume":"39","author":"Quellec","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref103","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-66179-7_61"},{"key":"ref104","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-56478-4"},{"key":"ref105","doi-asserted-by":"publisher","DOI":"10.1007\/s42979-022-01240-8"},{"key":"ref106","article-title":"Diabetic retinopathy detection through deep learning techniques: A review","volume":"20","author":"Alyoubi","year":"2020","journal-title":"Informat. Med. Unlocked"},{"key":"ref107","doi-asserted-by":"crossref","first-page":"3360","DOI":"10.1109\/ACCESS.2018.2888639","article-title":"Diagnosis of diabetic retinopathy using deep neural networks","volume":"7","author":"Gao","year":"2019","journal-title":"IEEE Access"},{"issue":"6","key":"ref108","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."},{"key":"ref109","volume-title":"Messidor-2: An Extension Dataset of the Messidor Database","author":"Decenci\u00e8re","year":"2016"},{"issue":"3","key":"ref110","doi-asserted-by":"crossref","first-page":"25","DOI":"10.3390\/data3030025","article-title":"Indian diabetic retinopathy image dataset (IDRiD): A database for diabetic retinopathy screening research","volume":"3","author":"Porwal","year":"2018","journal-title":"Data"},{"key":"ref111","volume-title":"Drive: Digital Retinal Images for Vessel Extraction","author":"Staal","year":"2004"},{"key":"ref112","volume-title":"Stare Project Database","author":"Hoover","year":"2003"},{"issue":"10","key":"ref113","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0274098","article-title":"Deep learning models for COVID-19 chest X-ray classification: Preventing shortcut learning using feature disentanglement","volume":"17","author":"Trivedi","year":"2022","journal-title":"PLoS ONE"},{"issue":"1","key":"ref114","first-page":"12598","article-title":"Federated learning in medicine: Facilitating multi-institutional collaborations without sharing patient data","volume-title":"Sci. Rep.","volume":"10","author":"Sheller","year":"2020"},{"key":"ref115","article-title":"Deep learning vessel segmentation and quantification of the foveal avascular zone using commercial and prototype OCT\u2014A platforms","author":"Heisler","year":"2019","journal-title":"arXiv:1909.11289"},{"issue":"2","key":"ref116","article-title":"Deep learning vessel segmentation and quantification of the foveal avascular zone in oct angiography","volume":"1","author":"Heisler","year":"2021","journal-title":"Ophthalmology Sci."},{"issue":"11","key":"ref117","doi-asserted-by":"crossref","first-page":"2835","DOI":"10.3390\/diagnostics12112835","article-title":"Federated learning in ocular imaging: Current progress and future direction","volume":"12","author":"Nguyen","year":"2022","journal-title":"Diagnostics"},{"issue":"11","key":"ref118","first-page":"39","article-title":"Collaborative diabetic retinopathy severity classification of optical coherence tomography angiography images","volume":"61","author":"Yu","year":"2020","journal-title":"Investigative Ophthalmology Vis. Sci."},{"issue":"2","key":"ref119","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1167\/tvst.9.2.38","article-title":"Microvasculature segmentation and intercapillary area quantification of the deep vascular complex using transfer learning","volume":"9","author":"Lo","year":"2020","journal-title":"Translational Vis. Sci. Technol."},{"key":"ref120","volume-title":"Explainable Ai (xai)","year":"2025"},{"issue":"1","key":"ref121","first-page":"1746","article-title":"Intelligent skin disease prediction system using transfer learning and explainable artificial intelligence","volume-title":"Sci. Rep.","volume":"15","author":"Abbas","year":"2025"},{"issue":"17","key":"ref122","doi-asserted-by":"crossref","first-page":"8749","DOI":"10.3390\/app12178749","article-title":"Explainable artificial intelligence enabled TeleOphthalmology for diabetic retinopathy grading and classification","volume":"12","author":"Obayya","year":"2022","journal-title":"Appl. Sci."}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/10820123\/11053490.pdf?arnumber=11053490","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,12]],"date-time":"2025-07-12T05:11:27Z","timestamp":1752297087000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11053490\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":122,"URL":"https:\/\/doi.org\/10.1109\/access.2025.3583647","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}