{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T19:23:27Z","timestamp":1771529007729,"version":"3.50.1"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,4,3]],"date-time":"2025-04-03T00:00:00Z","timestamp":1743638400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,4,3]],"date-time":"2025-04-03T00:00:00Z","timestamp":1743638400000},"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":["J Digit Imaging. Inform. med."],"DOI":"10.1007\/s10278-025-01467-w","type":"journal-article","created":{"date-parts":[[2025,4,5]],"date-time":"2025-04-05T05:48:18Z","timestamp":1743832098000},"page":"669-698","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Advancing Visual Perception Through VCANet-Crossover Osprey Algorithm: Integrating Visual Technologies"],"prefix":"10.1007","volume":"39","author":[{"given":"Yuwen","family":"Ning","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaxin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuyi","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,4,3]]},"reference":[{"issue":"12","key":"1467_CR1","doi-asserted-by":"publisher","first-page":"34291","DOI":"10.1007\/s11042-023-16813-9","volume":"83","author":"S Gupt","year":"2024","unstructured":"Gupt S, Thakur S, Gupta A: Comparative study of different machine learning models for automatic diabetic retinopathy detection using fundus image. Multimedia Tools and Applications, 83(12):34291-34322, 2024.","journal-title":"Multimedia Tools and Applications"},{"issue":"16","key":"1467_CR2","doi-asserted-by":"publisher","first-page":"5283","DOI":"10.3390\/s21165283","volume":"21","author":"T Nazir","year":"2021","unstructured":"Nazir T, Nawaz M, Rashid J, Mahum R, Masood M, Mehmood A, Ali F, Kim J, Kwon HY, Hussain A : Detection of diabetic eye disease from retinal images using a deep learning based CenterNet model. Sensors, 21(16): 5283, 2021.","journal-title":"Sensors"},{"issue":"1","key":"1467_CR3","first-page":"1119","volume":"35","author":"P Sarkar","year":"2023","unstructured":"Sarkar P, Dewangan O, Joshi A: A review on applications of artificial intelligence on bionic eye designing and functioning. Scandinavian Journal of Information Systems, 35(1):1119-1127, 2023.","journal-title":"Scandinavian Journal of Information Systems"},{"issue":"1","key":"1467_CR4","doi-asserted-by":"publisher","first-page":"2728719","DOI":"10.1155\/2023\/2728719","volume":"2023","author":"P Uppamma","year":"2023","unstructured":"Uppamma P, Bhattacharya S: Deep learning and medical image processing techniques for diabetic retinopathy: a survey of applications, challenges, and future trends. Journal of Healthcare Engineering, 2023(1): 2728719, 2023.","journal-title":"Journal of Healthcare Engineering"},{"key":"1467_CR5","doi-asserted-by":"crossref","unstructured":"Triwijoyo BK, Sabarguna BS, Budiharto W, Abdurachman E: Deep learning approach for classification of eye diseases based on color fundus images. In Diabetes and fundus OCT, 25\u201357, 2020","DOI":"10.1016\/B978-0-12-817440-1.00002-4"},{"issue":"1","key":"1467_CR6","doi-asserted-by":"publisher","first-page":"8276738","DOI":"10.1155\/2023\/8276738","volume":"2023","author":"V Narayan","year":"2023","unstructured":"Narayan V, Mall PK, Alkhayyat A, Abhishek K, Kumar S, Pandey P: [Retracted] Enhance\u2010Net: An Approach to Boost the Performance of Deep Learning Model Based on Real\u2010Time Medical Images. Journal of Sensors, 2023(1): 8276738, 2023.","journal-title":"Journal of Sensors"},{"issue":"11","key":"1467_CR7","doi-asserted-by":"publisher","first-page":"3704","DOI":"10.3390\/s21113704","volume":"21","author":"WL Alyoubi","year":"2021","unstructured":"Alyoubi WL, Abulkhair MF Shalash WM: Diabetic retinopathy fundus image classification and lesions localization system using deep learning. Sensors, 21(11): 3704, 2021.","journal-title":"Sensors"},{"key":"1467_CR8","doi-asserted-by":"crossref","unstructured":"Alharbi M: Multi-classification of eye disease based on fundus images using hybrid Squeeze Net and LRCN model. Multimedia Tools and Applications, 1\u201330, 2024.","DOI":"10.1007\/s11042-024-18281-1"},{"issue":"10","key":"1467_CR9","doi-asserted-by":"publisher","first-page":"30773","DOI":"10.1007\/s11042-023-16835-3","volume":"83","author":"S El-Ateif","year":"2024","unstructured":"El-Ateif S, Idri A : Eye diseases diagnosis using deep learning and multimodal medical eye imaging. Multimedia Tools and Applications, 83(10): 30773-30818, 2024.","journal-title":"Multimedia Tools and Applications"},{"key":"1467_CR10","doi-asserted-by":"publisher","first-page":"27590","DOI":"10.1109\/ACCESS.2023.3257988","volume":"11","author":"T Palaniswamy","year":"2023","unstructured":"Palaniswamy T, Vellingiri M: Internet of Things and Deep Learning Enabled Diabetic Retinopathy Diagnosis Using Retinal Fundus Images. IEEE Access, 11: 27590-27601, 2023.","journal-title":"IEEE Access"},{"key":"1467_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105602","volume":"146","author":"MR Islam","year":"2022","unstructured":"Islam MR, Abdulrazak LF, Nahiduzzaman M, Goni MOF, Anower M, Ahsan M, Haider J, Kowalski M: Applying supervised contrastive learning for the detection of diabetic retinopathy and its severity levels from fundus images. Computers in biology and medicine, 146: 105602, 2022.","journal-title":"Computers in biology and medicine"},{"issue":"19","key":"1467_CR12","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: EdgeSVDNet: 5G-enabled detection and classification of vision-threatening diabetic retinopathy in retinal fundus images. Electronics, 12(19) : 4094, 2023.","journal-title":"Electronics"},{"key":"1467_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.108099","volume":"171","author":"A Bilal","year":"2024","unstructured":"Bilal A, Liu X, Shafiq M, Ahmed Z, Long H: NIMEQ-SACNet: A novel self-attention precision medicine model for vision-threatening diabetic retinopathy using image data. Computers in Biology and Medicine, 171:108099, 2024.","journal-title":"Computers in Biology and Medicine"},{"key":"1467_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.102059","volume":"102","author":"S Al-Fahdawi","year":"2024","unstructured":"Al-Fahdawi S, Al-Waisy AS, Zeebaree DQ, Qahwaji R, Natiq H, Mohammed MA, Nedoma, J, Martinek R, Deveci M: Fundus-deepnet: Multi-label deep learning classification system for enhanced detection of multiple ocular diseases through data fusion of fundus images. Information Fusion, 102, p.102059, 2024.","journal-title":"Information Fusion"},{"issue":"1","key":"1467_CR15","doi-asserted-by":"publisher","first-page":"124","DOI":"10.3390\/diagnostics13010124","volume":"13","author":"SS Mondal","year":"2022","unstructured":"Mondal SS, Mandal N, Singh KK, Singh A, Izonin I: Edldr: An ensemble deep learning technique for detection and classification of diabetic retinopathy. Diagnostics 13(1):124, 2022","journal-title":"Diagnostics"},{"issue":"1","key":"1467_CR16","doi-asserted-by":"publisher","first-page":"6644071","DOI":"10.1155\/2021\/6644071","volume":"2021","author":"Y Xu","year":"2021","unstructured":"Xu Y, Zhou Z, Li X, Zhang N, Zhang M, Wei P: FFU\u2010net: feature fusion u\u2010net for lesion segmentation of diabetic retinopathy. BioMed Research International, 2021(1): 6644071, 2021","journal-title":"BioMed Research International"},{"issue":"3","key":"1467_CR17","doi-asserted-by":"publisher","first-page":"2651","DOI":"10.1007\/s40747-021-00318-9","volume":"9","author":"G Kalyani","year":"2023","unstructured":"Kalyani G, Janakiramaiah B, Karuna A, Prasad LN: Diabetic retinopathy detection and classification using capsule networks. Complex & Intelligent Systems, 9(3):2651-2664. 2023","journal-title":"Complex & Intelligent Systems"},{"issue":"1","key":"1467_CR18","doi-asserted-by":"publisher","first-page":"20892","DOI":"10.1038\/s41598-024-71650-6","volume":"14","author":"C Xu","year":"2024","unstructured":"Xu C, Guo X, Yang G, Cui Y, Su L, Dong H, Hu X, Che, S: Prior-guided attention fusion transformer for multi-lesion segmentation of diabetic retinopathy. Scientific Reports, 14(1):20892, 2024","journal-title":"Scientific Reports"},{"key":"1467_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.106489","volume":"96","author":"J Qiu","year":"2024","unstructured":"Qiu, J, Huang, W, Huang, Y, Yu N, Tang X: AugPaste: A one-shot approach for diabetic retinopathy detection. Biomedical Signal Processing and Control, 96: 106489, 2024","journal-title":"Biomedical Signal Processing and Control"},{"issue":"1","key":"1467_CR20","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1049\/cit2.12155","volume":"9","author":"X Luo","year":"2024","unstructured":"Luo X, Wang W, Xu Y, Lai Z, Jin X, Zhang B, Zhang D: A deep convolutional neural network for diabetic retinopathy detection via mining local and long\u2010range dependence. CAAI Transactions on Intelligence Technology, 9(1): 153-166, 2024","journal-title":"CAAI Transactions on Intelligence Technology"},{"key":"1467_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2024.108160","volume":"249","author":"R Romero-Ora\u00e1","year":"2024","unstructured":"Romero-Ora\u00e1 R, Herrero-Tudela M, L\u00f3pez MI, Hornero R, Garc\u00eda M: Attention-based deep learning framework for automatic fundus image processing to aid in diabetic retinopathy grading. Computer Methods and Programs in Biomedicine, 249: 108160, 2024","journal-title":"Computer Methods and Programs in Biomedicine"},{"issue":"1","key":"1467_CR22","first-page":"160","volume":"16","author":"S Lei","year":"2024","unstructured":"Lei S, Lei X, Chen M, Pan Y: Drug Repositioning Based on Deep Sparse Autoencoder and Drug\u2013Disease Similarity. Interdisciplinary Sciences: Computational Life Sciences, 16(1): 160-175, 2024","journal-title":"Interdisciplinary Sciences: Computational Life Sciences"},{"key":"1467_CR23","first-page":"13073","volume":"33","author":"J Dapello","year":"2020","unstructured":"Dapello J, Marques T, Schrimpf M, Geiger F, Cox D, DiCarlo JJ: Simulating a primary visual cortex at the front of CNNs improves robustness to image perturbations. Advances in Neural Information Processing Systems, 33:13073-13087, 2020","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"4","key":"1467_CR24","doi-asserted-by":"publisher","first-page":"494","DOI":"10.3390\/sym16040494","volume":"16","author":"C Shi","year":"2024","unstructured":"Shi C, Liu Z, Qu J, Deng Y: The Expansion Methods of Inception and Its Application. Symmetry, 16(4): 494, 2024","journal-title":"Symmetry"},{"key":"1467_CR25","doi-asserted-by":"publisher","first-page":"1126450","DOI":"10.3389\/fmech.2022.1126450","volume":"8","author":"M Dehghani","year":"2023","unstructured":"Dehghani M, Trojovsk\u00fd, P, Osprey optimization algorithm: A new bio-inspired metaheuristic algorithm for solving engineering optimization problems. Frontiers in Mechanical Engineering, 8 :1126450, 2023.","journal-title":"Frontiers in Mechanical Engineering"},{"issue":"1","key":"1467_CR26","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1093\/jcde\/qwac112","volume":"10","author":"H Su","year":"2023","unstructured":"Su H, Zhao D, Yu F, Heidari AA, Xu Z, Alotaibi FS, Mafarja M. Che H: A horizontal and vertical crossover cuckoo search: Optimizing performance for the engineering problems. Journal of Computational Design and Engineering, 10(1): 36-64, 2023.","journal-title":"Journal of Computational Design and Engineering"},{"key":"1467_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106840","volume":"159","author":"L Li","year":"2023","unstructured":"Li L, Ma K, Song Y, Du X: TSRL-Net: Target-aware supervision residual learning for stroke segmentation. Computers in Biology and Medicine, 159: 106840, 2023.","journal-title":"Computers in Biology and Medicine"},{"key":"1467_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.104952","volume":"113","author":"H Gezici","year":"2022","unstructured":"Gezici H, Livatyali H: An improved Harris Hawks Optimization algorithm for continuous and discrete optimization problems. Engineering Applications of Artificial Intelligence, 113: 104952, 2022.","journal-title":"Engineering Applications of Artificial Intelligence"},{"issue":"5","key":"1467_CR29","doi-asserted-by":"publisher","first-page":"1522","DOI":"10.1007\/s42235-022-00207-y","volume":"19","author":"SK Sahoo","year":"2022","unstructured":"Sahoo SK, Saha AK : A hybrid moth flame optimization algorithm for global optimization. Journal of Bionic Engineering, 19(5):1522-1543, 2022.","journal-title":"Journal of Bionic Engineering"},{"key":"1467_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105858","volume":"148","author":"MH Nadimi-Shahraki","year":"2022","unstructured":"Nadimi-Shahraki MH, Zamani H, Mirjalili S: Enhanced whale optimization algorithm for medical feature selection: A COVID-19 case study. Computers in biology and medicine, 148: 105858, 2022.","journal-title":"Computers in biology and medicine"},{"issue":"19","key":"1467_CR31","doi-asserted-by":"publisher","first-page":"27949","DOI":"10.1007\/s11042-022-12966-1","volume":"81","author":"YN Pawan","year":"2022","unstructured":"Pawan YN, Prakash KB, Chowdhury S, Hu YC: Particle swarm optimization performance improvement using deep learning techniques. Multimedia Tools and Applications, 81(19): 27949-27968, 2022.","journal-title":"Multimedia Tools and Applications"}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-025-01467-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-025-01467-w","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-025-01467-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T18:46:33Z","timestamp":1771526793000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-025-01467-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,3]]},"references-count":31,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["1467"],"URL":"https:\/\/doi.org\/10.1007\/s10278-025-01467-w","relation":{},"ISSN":["2948-2933"],"issn-type":[{"value":"2948-2933","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,3]]},"assertion":[{"value":"30 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 February 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 February 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 April 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"This article does not contain any studies with human or animal subjects performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human and Animal Rights"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}