{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T05:45:44Z","timestamp":1784094344534,"version":"3.55.0"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T00:00:00Z","timestamp":1746662400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T00:00:00Z","timestamp":1746662400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int Ophthalmol"],"DOI":"10.1007\/s10792-025-03528-z","type":"journal-article","created":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T08:14:36Z","timestamp":1746692076000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Non-proliferative diabetic retinopathy detection using Rosmarus Quagga optimized explainable generative meta learning based deep convolutional neural network model"],"prefix":"10.1007","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-5243-1530","authenticated-orcid":false,"given":"Ajita Arvind","family":"Mahapadi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5251-1935","authenticated-orcid":false,"given":"Vishal","family":"Shirsath","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9845-8412","authenticated-orcid":false,"given":"Ajitkumar","family":"Pundge","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,5,8]]},"reference":[{"key":"3528_CR1","doi-asserted-by":"publisher","first-page":"55522","DOI":"10.1109\/ACCESS.2022.3177651","volume":"10","author":"MD Alahmadi","year":"2022","unstructured":"Alahmadi MD (2022) Texture attention network for diabetic retinopathy classification. IEEE Access 10:55522\u201355532","journal-title":"IEEE Access"},{"key":"3528_CR2","doi-asserted-by":"publisher","first-page":"83004","DOI":"10.1109\/ACCESS.2023.3301618","volume":"11","author":"WK Wong","year":"2023","unstructured":"Wong WK, Juwono FH, Capriono C (2023) Diabetic retinopathy detection and grading: a transfer learning approach using simultaneous parameter optimization and feature-weighted ECOC ensemble. IEEE Access 11:83004\u201383016","journal-title":"IEEE Access"},{"issue":"10","key":"3528_CR3","doi-asserted-by":"publisher","first-page":"1608","DOI":"10.1016\/j.ophtha.2018.04.007","volume":"125","author":"TY Wong","year":"2018","unstructured":"Wong TY, Sun J, Kawasaki R, Ruamviboonsuk P, Gupta N, Lansingh VC, Maia M, Mathenge W, Moreker S, Muqit MM, Resnikoff S (2018) Guidelines on diabetic eye care: the international council of ophthalmology recommendations for screening, follow-up, referral, and treatment based on resource settings. Ophthalmology 125(10):1608\u20131622","journal-title":"Ophthalmology"},{"key":"3528_CR4","doi-asserted-by":"publisher","first-page":"111485","DOI":"10.1016\/j.measurement.2022.111485","volume":"200","author":"M Murugappan","year":"2022","unstructured":"Murugappan M, Prakash NB, Jeya R, Mohanarathinam A, Hemalakshmi GR, Mahmud M (2022) A novel few-shot classification framework for diabetic retinopathy detection and grading. Measurement 200:111485","journal-title":"Measurement"},{"key":"3528_CR5","doi-asserted-by":"publisher","first-page":"100377","DOI":"10.1016\/j.imu.2020.100377","volume":"20","author":"WL Alyoubi","year":"2020","unstructured":"Alyoubi WL, Shalash WM, Abulkhair MF (2020) Diabetic retinopathy detection through deep learning techniques: a review. Inf Med Unlocked 20:100377","journal-title":"Inf Med Unlocked"},{"key":"3528_CR6","doi-asserted-by":"publisher","first-page":"40019","DOI":"10.1109\/ACCESS.2024.3373467","volume":"12","author":"A Jabbar","year":"2024","unstructured":"Jabbar A, Liaqat HB, Akram A, Sana MU, Azp\u00edroz ID, Diez IDLT, Ashraf I (2024) A lesion-based diabetic retinopathy detection through hybrid deep learning model. IEEE Access 12:40019\u201340036","journal-title":"IEEE Access"},{"issue":"6","key":"3528_CR7","doi-asserted-by":"publisher","first-page":"1138","DOI":"10.1038\/s41433-018-0064-9","volume":"32","author":"R Rajalakshmi","year":"2018","unstructured":"Rajalakshmi R, Subashini R, Anjana RM, Mohan V (2018) Automated diabetic retinopathy detection in smartphone-based fundus photography using artificial intelligence. Eye 32(6):1138\u20131144","journal-title":"Eye"},{"key":"3528_CR8","doi-asserted-by":"publisher","unstructured":"Venyo AKG (2023) Diabetes mellitus: a review and update. Journal of Ophthalmology Research Reviews & Reports. SRC\/JORRR-155 144(4):2\u201324. https:\/\/doi.org\/10.47363\/JORRR\/2023","DOI":"10.47363\/JORRR\/2023"},{"key":"3528_CR9","doi-asserted-by":"publisher","first-page":"113172","DOI":"10.1109\/ACCESS.2022.3217216","volume":"10","author":"H Mustafa","year":"2022","unstructured":"Mustafa H, Ali SF, Bilal M, Hanif MS (2022) Multi-stream deep neural network for diabetic retinopathy severity classification under a boosting framework. IEEE Access 10:113172\u2013113183","journal-title":"IEEE Access"},{"issue":"2","key":"3528_CR10","doi-asserted-by":"publisher","first-page":"20180011","DOI":"10.1515\/bams-2018-0011","volume":"14","author":"S Sharma","year":"2018","unstructured":"Sharma S, Maheshwari S, Shukla A (2018) An intelligible deep convolution neural network-based approach for classification of diabetic retinopathy. Bio Algorithms Med Syst 14(2):20180011","journal-title":"Bio Algorithms Med Syst"},{"issue":"1","key":"3528_CR11","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.ophtha.2017.06.029","volume":"125","author":"DS Dhoot","year":"2018","unstructured":"Dhoot DS, Baker K, Saroj N, Vitti R, Berliner AJ, Metzig C, Thompson D, Singh RP (2018) Baseline factors affecting changes in diabetic retinopathy severity scale score after intravitreal aflibercept or laser for diabetic macular edema: post hoc analyses from VISTA and VIVID. Ophthalmology 125(1):51\u201356","journal-title":"Ophthalmology"},{"key":"3528_CR12","doi-asserted-by":"publisher","first-page":"117546","DOI":"10.1109\/ACCESS.2023.3326528","volume":"11","author":"W Nazih","year":"2023","unstructured":"Nazih W, Aseeri AO, Atallah OY, El-Sappagh S (2023) Vision transformer model for predicting the severity of diabetic retinopathy in fundus photography-based retina images. IEEE Access 11:117546\u2013117561","journal-title":"IEEE Access"},{"key":"3528_CR13","doi-asserted-by":"publisher","first-page":"49445","DOI":"10.1109\/ACCESS.2022.3172789","volume":"10","author":"E Trojovsk\u00e1","year":"2022","unstructured":"Trojovsk\u00e1 E, Dehghani M, Trojovsk\u00fd P (2022) Zebra optimization algorithm: a new bio-inspired optimization algorithm for solving optimization algorithm. IEEE Access 10:49445\u201349473","journal-title":"IEEE Access"},{"key":"3528_CR14","unstructured":"Diabetic Retinopathy Detection dataset: https:\/\/www.kaggle.com\/c\/diabetic-retinopathy-detection\/data. Accessed on May 2024."},{"key":"3528_CR15","unstructured":"IDRiD dataset: https:\/\/www.kaggle.com\/datasets\/mariaherrerot\/idrid-dataset. Accessed on May 2024"},{"issue":"9","key":"3528_CR16","doi-asserted-by":"publisher","first-page":"23544","DOI":"10.1109\/ACCESS.2021.3056186","volume":"1","author":"A Bilal","year":"2021","unstructured":"Bilal A, Sun G, Li Y, Mazhar S, Khan AQ (2021) Diabetic retinopathy detection and classification using mixed models for a disease grading database. IEEE Access 1(9):23544\u201323553","journal-title":"IEEE Access"},{"key":"3528_CR17","doi-asserted-by":"publisher","first-page":"20247","DOI":"10.1109\/ACCESS.2022.3151644","volume":"10","author":"MCS Tang","year":"2022","unstructured":"Tang MCS, Teoh SS, Ibrahim H, Embong Z (2022) A deep learning approach for the detection of neovascularization in fundus images using transfer learning. IEEE Access 10:20247\u201320258","journal-title":"IEEE Access"},{"key":"3528_CR18","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.artmed.2019.03.009","volume":"96","author":"R Pires","year":"2019","unstructured":"Pires R, Avila S, Wainer J, Valle E, Abramoff MD, Rocha A (2019) A data-driven approach to referable diabetic retinopathy detection. Artif Intell Med 96:93\u2013106","journal-title":"Artif Intell Med"},{"issue":"6","key":"3528_CR19","doi-asserted-by":"publisher","first-page":"663","DOI":"10.1080\/21681163.2021.2021111","volume":"10","author":"A Bilal","year":"2022","unstructured":"Bilal A, Sun G, Mazhar S, Imran A, Latif J (2022) A Transfer Learning and U-Net-based automatic detection of diabetic retinopathy from fundus images. Comput Methods Biomech Biomed Eng Imaging Vis 10(6):663\u2013674","journal-title":"Comput Methods Biomech Biomed Eng Imaging Vis"},{"issue":"7","key":"3528_CR20","doi-asserted-by":"publisher","first-page":"1427","DOI":"10.3390\/sym14071427","volume":"14","author":"A Bilal","year":"2022","unstructured":"Bilal A, Zhu L, Deng A, Lu H, Wu N (2022) AI-based automatic detection and classification of diabetic retinopathy using U-Net and deep learning. Symmetry 14(7):1427","journal-title":"Symmetry"},{"key":"3528_CR21","doi-asserted-by":"publisher","first-page":"105069","DOI":"10.1109\/ACCESS.2023.3317348","volume":"11","author":"K Aurangzeb","year":"2023","unstructured":"Aurangzeb K, Alharthi R, Haider SI, Alhussein M (2023) Systematic development of AI-enabled diagnostic systems for glaucoma and diabetic retinopathy. IEEE Access 11:105069\u2013105081","journal-title":"IEEE Access"},{"issue":"1","key":"3528_CR22","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1186\/s12880-024-01406-1","volume":"24","author":"V Thanikachalam","year":"2024","unstructured":"Thanikachalam V, Kabilan K, Erramchetty SK (2024) Optimized deep CNN for detection and classification of diabetic retinopathy and diabetic macular edema. BMC Med Imaging 24(1):227","journal-title":"BMC Med Imaging"},{"issue":"1","key":"3528_CR23","doi-asserted-by":"publisher","first-page":"e0295951","DOI":"10.1371\/journal.pone.0295951","volume":"19","author":"A Bilal","year":"2024","unstructured":"Bilal A, Imran A, Baig TI, Liu X, Long H, Alzahrani A, Shafiq M (2024) Improved support vector machine based on CNN-SVD for vision-threatening diabetic retinopathy detection and classification. PLoS ONE 19(1):e0295951","journal-title":"PLoS ONE"},{"issue":"5","key":"3528_CR24","doi-asserted-by":"publisher","first-page":"100303","DOI":"10.1016\/j.health.2024.100303","volume":"1","author":"FJ Shamrat","year":"2024","unstructured":"Shamrat FJ, Shakil R, Akter B, Ahmed MZ, Ahmed K, Bui FM, Moni MA (2024) An advanced deep neural network for fundus image analysis and enhancing diabetic retinopathy detection. Healthc Anal 1(5):100303","journal-title":"Healthc Anal"},{"issue":"1","key":"3528_CR25","doi-asserted-by":"publisher","first-page":"8775","DOI":"10.1038\/s41598-023-35863-5","volume":"13","author":"P Trojovsk\u00fd","year":"2023","unstructured":"Trojovsk\u00fd P, Dehghani M (2023) A new bio-inspired metaheuristic algorithm for solving optimization problems based on walruses\u2019 behavior. Sci Rep 13(1):8775","journal-title":"Sci Rep"},{"key":"3528_CR26","doi-asserted-by":"crossref","unstructured":"Arora M, Pandey M (2019) Deep neural network for diabetic retinopathy detection. In: 2019 international conference on machine learning, big data, cloud and parallel computing (COMITCon). IEEE, pp 189\u2013193","DOI":"10.1109\/COMITCon.2019.8862217"},{"key":"3528_CR27","doi-asserted-by":"crossref","unstructured":"Kwasigroch A, Jarzembinski B, Grochowski M (2018) Deep CNN based decision support system for detection and assessing the stage of diabetic retinopathy. In: 2018 International interdisciplinary PhD workshop (IIPhDW). IEEE, pp 111\u2013116","DOI":"10.1109\/IIPHDW.2018.8388337"},{"issue":"19","key":"3528_CR28","doi-asserted-by":"publisher","first-page":"4094","DOI":"10.3390\/electronics12194094","volume":"12","author":"A Bilal","year":"2023","unstructured":"Bilal A, Liu X, Baig TI, Long H, Shafiq M (2023) EdgeSVDNet: 5G-enabled detection and classification of vision-threatening diabetic retinopathy in retinal fundus images. Electronics 12(19):4094","journal-title":"Electronics"},{"key":"3528_CR29","doi-asserted-by":"crossref","unstructured":"Bilal A, Sun G, Mazhar S, Imran A (2022) Improved grey wolf optimization-based feature selection and classification using CNN for diabetic retinopathy detection. In: Evolutionary computing and mobile sustainable networks: proceedings of ICECMSN 2021. Singapore: Springer Singapore, pp 1\u201314","DOI":"10.1007\/978-981-16-9605-3_1"},{"key":"3528_CR30","first-page":"939","volume":"13","author":"P Enkvetchakul","year":"2022","unstructured":"Enkvetchakul P, Surinta O, Noppitak S (2022) Effective data resampling and meta-learning convolutional neural networks for diabetic retinopathy recognition. ICIC Express Lett Part B Appl 13:939\u2013948","journal-title":"ICIC Express Lett Part B Appl"}],"container-title":["International Ophthalmology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10792-025-03528-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10792-025-03528-z","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10792-025-03528-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T18:05:27Z","timestamp":1766167527000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10792-025-03528-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,8]]},"references-count":30,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["3528"],"URL":"https:\/\/doi.org\/10.1007\/s10792-025-03528-z","relation":{},"ISSN":["1573-2630"],"issn-type":[{"value":"1573-2630","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,8]]},"assertion":[{"value":"26 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 April 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 May 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"178"}}