{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T09:19:30Z","timestamp":1787908770495,"version":"build-2784847793"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,8,5]],"date-time":"2025-08-05T00:00:00Z","timestamp":1754352000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,5]],"date-time":"2025-08-05T00:00:00Z","timestamp":1754352000000},"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\/s10792-025-03660-w","type":"journal-article","created":{"date-parts":[[2025,8,5]],"date-time":"2025-08-05T12:35:20Z","timestamp":1754397320000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Retinal image-based disease classification using hybrid deep architecture with improved image features"],"prefix":"10.1007","volume":"45","author":[{"given":"L B","family":"Lisha","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sylaja Vallee Narayan S","family":"R","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,8,5]]},"reference":[{"issue":"1","key":"3660_CR1","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1016\/j.icte.2021.05.002","volume":"8","author":"SSM Sheet","year":"2022","unstructured":"Sheet SSM, Tan TS, As\u2019ari MA, Hitam WHW, Sia JS (2022) Retinal disease identification using upgraded CLAHE filter and transfer convolution neural network. Ict Express 8(1):142\u2013150","journal-title":"Ict Express"},{"issue":"1","key":"3660_CR2","doi-asserted-by":"publisher","first-page":"150","DOI":"10.3390\/s24010150","volume":"24","author":"J Peng","year":"2023","unstructured":"Peng J, Lu J, Zhuo J, Li P (2023) Multi-scale-denoising residual convolutional network for retinal disease classification using OCT. Sensors 24(1):150","journal-title":"Sensors"},{"key":"3660_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00521-024-09820-w","volume":"36","author":"O Sivaz","year":"2024","unstructured":"Sivaz O, Aykut M (2024) Combining EfficientNet with ML-decoder classification head for multi-label retinal disease classification. Neural Comput Appl 36:1\u201311","journal-title":"Neural Comput Appl"},{"key":"3660_CR4","doi-asserted-by":"publisher","first-page":"102059","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, Deveci M (2024) Fundus-Deepnet: Multi-label deep learning classification system for enhanced detection of multiple ocular diseases through data fusion of fundus images. Inform Fusion 102:102059","journal-title":"Inform Fusion"},{"issue":"10","key":"3660_CR5","doi-asserted-by":"publisher","first-page":"9688","DOI":"10.1016\/j.jksuci.2021.12.002","volume":"34","author":"PK Upadhyay","year":"2022","unstructured":"Upadhyay PK, Rastogi S, Kumar KV (2022) Coherent convolution neural network based retinal disease detection using optical coherence tomographic images. J King Saud Univers-Comput Inform Sci 34(10):9688\u20139695","journal-title":"J King Saud Univers-Comput Inform Sci"},{"key":"3660_CR6","doi-asserted-by":"publisher","first-page":"100174","DOI":"10.1016\/j.health.2023.100174","volume":"3","author":"MSB Phridviraj","year":"2023","unstructured":"Phridviraj MSB, Bhukya R, Madugula S, Manjula A, Vodithala S, Waseem MS (2023) A bi-directional long short-term memory-based diabetic retinopathy detection model using retinal fundus images. Healthc Anal 3:100174","journal-title":"Healthc Anal"},{"issue":"1","key":"3660_CR7","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1007\/s44196-023-00210-z","volume":"16","author":"\u0130 Kayadibi","year":"2023","unstructured":"Kayadibi \u0130, G\u00fcraks\u0131n GE (2023) An explainable fully dense fusion neural network with deep support vector machine for retinal disease determination. Int J Comput Intell Syst 16(1):28","journal-title":"Int J Comput Intell Syst"},{"issue":"1","key":"3660_CR8","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1186\/s44147-023-00335-0","volume":"71","author":"R Chavan","year":"2024","unstructured":"Chavan R, Pete D (2024) Automatic multi-disease classification on retinal images using multilevel glowworm swarm convolutional neural network. J Eng Appl Sci 71(1):26","journal-title":"J Eng Appl Sci"},{"key":"3660_CR9","doi-asserted-by":"publisher","first-page":"57497","DOI":"10.1109\/ACCESS.2020.2979753","volume":"8","author":"S Gayathri","year":"2020","unstructured":"Gayathri S, Krishna AK, Gopi VP, Palanisamy P (2020) Automated binary and multiclass classification of diabetic retinopathy using Haralick and multiresolution features. IEEE Access 8:57497\u201357504","journal-title":"IEEE Access"},{"issue":"16","key":"3660_CR10","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, Hussain A (2021) Detection of diabetic eye disease from retinal images using a deep learning based CenterNet model. Sensors 21(16):5283","journal-title":"Sensors"},{"key":"3660_CR11","doi-asserted-by":"publisher","first-page":"35778","DOI":"10.1109\/ACCESS.2021.3061690","volume":"9","author":"G Zhang","year":"2021","unstructured":"Zhang G, Pan J, Zhang Z, Zhang H, Xing C, Sun B, Li M (2021) Hybrid graph convolutional network for semi-supervised retinal image classification. IEEE Access 9:35778\u201335789","journal-title":"IEEE Access"},{"key":"3660_CR12","doi-asserted-by":"publisher","first-page":"100140","DOI":"10.1016\/j.health.2023.100140","volume":"3","author":"R Thanki","year":"2023","unstructured":"Thanki R (2023) A deep neural network and machine learning approach for retinal fundus image classification. Healthc Anal 3:100140","journal-title":"Healthc Anal"},{"issue":"16","key":"3660_CR13","doi-asserted-by":"publisher","first-page":"9171","DOI":"10.1007\/s00521-024-09564-7","volume":"36","author":"GR Hemalakshmi","year":"2024","unstructured":"Hemalakshmi GR, Murugappan M, Sikkandar MY, Begum SS, Prakash NB (2024) Automated retinal disease classification using hybrid transformer model (SViT) using optical coherence tomography images. Neural Comput Appl 36(16):9171\u20139188","journal-title":"Neural Comput Appl"},{"issue":"3","key":"3660_CR14","doi-asserted-by":"publisher","first-page":"100077","DOI":"10.1016\/j.aopr.2022.100077","volume":"2","author":"J Fu","year":"2022","unstructured":"Fu J, Cao L, Wei S, Xu M, Song Y, Li H, You Y (2022) A GAN-based deep enhancer for quality enhancement of retinal images photographed by a handheld fundus camera. Adv Ophthalmol Pract Res 2(3):100077","journal-title":"Adv Ophthalmol Pract Res"},{"key":"3660_CR15","unstructured":"Kyung-Ah S, Lee TK, Kim SY, Choi HJ, Choe EK (2024) Vision transformer-based interpretable metabolic syndrome classification using retinal images"},{"key":"3660_CR16","unstructured":"Jos\u00e9 M, Aresta G, Bogunovi\u0107 H (2024) RRWNet: recursive refinement network for effective retinal artery\/vein segmentation and classification. arXiv preprint arXiv:2402.03166"},{"key":"3660_CR17","doi-asserted-by":"crossref","unstructured":"Amit C, Ahlawat S, Urooj S, Pathak N, Lay-Ekuakille A, Sharma N (2023) A deep learning-based framework for retinal disease classification. In: Healthcare, vol. 11, no. 2, MDPI","DOI":"10.3390\/healthcare11020212"},{"key":"3660_CR18","doi-asserted-by":"crossref","unstructured":"Shuxian L, Wang W, Deng L, Xu H (2024) Cnn-trans model: a parallel dual-branch network for fundus image classification. In: Biomedical signal processing and control, vol. 96","DOI":"10.1016\/j.bspc.2024.106621"},{"issue":"10","key":"3660_CR19","doi-asserted-by":"publisher","first-page":"203","DOI":"10.3390\/jimaging9100203","volume":"9","author":"Z Li","year":"2023","unstructured":"Li Z, Han Y, Yang X (2023) Multi-fundus diseases classification using retinal optical coherence tomography images with Swin transformer V2. J Imag 9(10):203","journal-title":"J Imag"},{"key":"3660_CR20","first-page":"92352","volume":"8","author":"Y Luo","year":"2020","unstructured":"Luo Y, Pan J, Fan S, Du Z, Zhang G (2020) Retinal image classification by self-supervised fuzzy clustering network. IEEE Access 8:92352\u201392362","journal-title":"IEEE Access"},{"key":"3660_CR21","doi-asserted-by":"publisher","first-page":"S58","DOI":"10.1016\/j.optom.2022.11.001","volume":"15","author":"AE Chowdhury","year":"2022","unstructured":"Chowdhury AE, Mann G, Morgan WH, Vukmirovic A, Mehnert A, Sohel F (2022) MSGANet-RAV: a multiscale guided attention network for artery-vein segmentation and classification from optic disc and retinal images. J Optometry 15:S58\u2013S69","journal-title":"J Optometry"},{"key":"3660_CR22","doi-asserted-by":"publisher","first-page":"108353","DOI":"10.1016\/j.engappai.2024.108353","volume":"133","author":"M Arsalan","year":"2024","unstructured":"Arsalan M, Haider A, Park C, Hong JS, Park KR (2024) Multiscale triplet spatial information fusion-based deep learning method to detect retinal pigment signs with fundus images. Eng Appl Artif Intell 133:108353","journal-title":"Eng Appl Artif Intell"},{"key":"3660_CR23","unstructured":"Huang, Luffina C, Darren J, Chiu, Mehta M (2024) Self-supervised learning featuring small-scale image dataset for treatable retinal diseases classification.\u201d arXiv preprint arXiv:2404.10166"},{"issue":"1","key":"3660_CR24","doi-asserted-by":"publisher","first-page":"20712","DOI":"10.1038\/s41598-023-48039-y","volume":"13","author":"K Radha","year":"2023","unstructured":"Radha K, Yepuganti K, Saritha S, Kamireddy C, Bavirisetti DP (2023) Unfolded deep kernel estimation-attention UNet-based retinal image segmentation. Scientif Rep 13(1):20712","journal-title":"Scientif Rep"},{"key":"3660_CR25","unstructured":"Yap, Peng B, Beng Koon N (2021) Semi-weakly supervised contrastive representation learning for retinal fundus images. arXiv preprint arXiv:2108.02122"},{"issue":"2","key":"3660_CR26","doi-asserted-by":"publisher","first-page":"212","DOI":"10.3390\/healthcare11020212","volume":"11","author":"A Choudhary","year":"2023","unstructured":"Choudhary A, Ahlawat S, Urooj S, Pathak N, Lay-Ekuakille A, Sharma N (2023) A deep learning-based framework for retinal disease classification. Healthcare MDPI 11(2):212","journal-title":"Healthcare MDPI"},{"issue":"2","key":"3660_CR27","doi-asserted-by":"publisher","first-page":"15","DOI":"10.4018\/IJAEC.2020040102","volume":"11","author":"LK Singh","year":"2020","unstructured":"Singh LK, Garg H (2020) Detection of glaucoma in retinal images based on multiobjective approach. Int J Appl Evolut Comput (IJAEC) 11(2):15\u201327","journal-title":"Int J Appl Evolut Comput (IJAEC)"},{"issue":"1","key":"3660_CR28","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0318264","volume":"20","author":"Z Liu","year":"2025","unstructured":"Liu Z, Gao A, Sheng H, Wang X (2025) Identification of diabetic retinopathy lesions in fundus images by integrating CNN and vision mamba models. PLoS ONE 20(1):e0318264","journal-title":"PLoS ONE"},{"key":"3660_CR29","doi-asserted-by":"crossref","unstructured":"Huang C, Sarabi M, Ragab AE (2024) MobileNet-V2\/IFHO model for accurate detection of early-stage diabetic retinopathy. In: Heliyon, 10(17)","DOI":"10.1016\/j.heliyon.2024.e37293"},{"issue":"1","key":"3660_CR30","doi-asserted-by":"publisher","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":"19","key":"3660_CR31","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"},{"issue":"4","key":"3660_CR32","doi-asserted-by":"publisher","first-page":"3752","DOI":"10.11591\/ijece.v12i4.pp3752-3761","volume":"12","author":"K Kusrini","year":"2022","unstructured":"Kusrini K, Yudianto MRA, Al Fatta H (2022) The effect of Gaussian filter and data preprocessing on the classification of Punakawan puppet images with the convolutional neural network algorithm. Int J Electric Compute Eng 12(4):3752","journal-title":"Int J Electric Compute Eng"},{"key":"3660_CR33","doi-asserted-by":"publisher","first-page":"11417","DOI":"10.1109\/JSTARS.2021.3117975","volume":"14","author":"T Liu","year":"2021","unstructured":"Liu T, Chen T, Niu R, Plaza A (2021) Landslide detection mapping employing CNN, ResNet, and DenseNet in the three gorges reservoir, China. IEEE J Select Top Appl Earth Observ Remote Sens 14:11417\u201311428","journal-title":"IEEE J Select Top Appl Earth Observ Remote Sens"},{"key":"3660_CR34","doi-asserted-by":"publisher","first-page":"110055","DOI":"10.1016\/j.asoc.2023.110055","volume":"136","author":"GS Nijaguna","year":"2023","unstructured":"Nijaguna GS, Babu JA, Parameshachari BD, de Prado RP, Frnda J (2023) Quantum fruit fly algorithm and ResNet50-VGG16 for medical diagnosis. Appl Soft Comput 136:110055","journal-title":"Appl Soft Comput"},{"issue":"1","key":"3660_CR35","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1002\/ima.22081","volume":"24","author":"A Jayachandran","year":"2014","unstructured":"Jayachandran A, Dhanasekaran R (2014) Brain tumor severity analysis using modified multi-texton histogram and hybrid kernel SVM. Int J Imaging Syst Technol 24(1):72\u201382","journal-title":"Int J Imaging Syst Technol"},{"key":"3660_CR36","doi-asserted-by":"publisher","first-page":"764","DOI":"10.1016\/j.procs.2015.06.090","volume":"54","author":"N Dhanachandra","year":"2015","unstructured":"Dhanachandra N, Manglem K, Chanu YJ (2015) Image segmentation using K-means clustering algorithm and subtractive clustering algorithm. Procedia Comput Sci 54:764\u2013771","journal-title":"Procedia Comput Sci"},{"key":"3660_CR37","doi-asserted-by":"crossref","unstructured":"Bimantoro, Fitri, Ashri Annisaak Aziz, Husodo AY, Musnansyah A, Minarno AE, Kurniawardhani A (2020) Image retrieval using modified multi Texton and rotation invariant local binary pattern. In: 2020 international conference on advancement in data science, e-learning and information systems (ICADEIS), IEEE, pp. 1\u20135","DOI":"10.1109\/ICADEIS49811.2020.9277462"},{"issue":"4","key":"3660_CR38","doi-asserted-by":"publisher","first-page":"915","DOI":"10.5755\/j01.itc.52.4.34232","volume":"52","author":"B Krishnakumar","year":"2023","unstructured":"Krishnakumar B, Kousalya K (2023) Optimal trained deep learning model for breast cancer segmentation and classification. Inform Technol Contr 52(4):915\u2013934","journal-title":"Inform Technol Contr"},{"key":"3660_CR39","doi-asserted-by":"publisher","first-page":"103053","DOI":"10.1016\/j.bspc.2021.103053","volume":"70","author":"B Topta\u015f","year":"2021","unstructured":"Topta\u015f B, Hanbay D (2021) Retinal blood vessel segmentation using pixel-based feature vector. Biomed Signal Proces Contr 70:103053","journal-title":"Biomed Signal Proces Contr"},{"issue":"3","key":"3660_CR40","first-page":"67","volume":"11","author":"L Zhang","year":"2023","unstructured":"Zhang L, Liang X (2023) Image segmentation of plant leaves in natural environments based on LinkNet. J Comput Electron Inform Manag 11(3):67\u201372","journal-title":"J Comput Electron Inform Manag"},{"key":"3660_CR41","doi-asserted-by":"publisher","first-page":"35","DOI":"10.5194\/isprs-archives-XLIII-B3-2020-35-2020","volume":"43","author":"Yi Ding","year":"2020","unstructured":"Ding Yi, Muyu Wu, Yongshu Xu, Duan S (2020) P-linknet: Linknet with spatial pyramid pooling for high-resolution satellite imagery. Int Arch Photogramm Remote Sens Spat Inf Sci 43:35\u201340","journal-title":"Int Arch Photogramm Remote Sens Spat Inf Sci"},{"key":"3660_CR42","unstructured":"Chigozie N, Ijomah W, Gachagan A, Marshall S (2018) Activation functions: Comparison of trends in practice and research for deep learning. arXiv preprint arXiv:1811.03378"},{"issue":"11","key":"3660_CR43","doi-asserted-by":"publisher","first-page":"2746","DOI":"10.3390\/biomedicines10112746","volume":"10","author":"LS Bernardo","year":"2022","unstructured":"Bernardo LS, Dama\u0161evi\u010dius R, Ling SH, de Albuquerque VHC, Tavares JMR (2022) Modified SqueezeNet architecture for Parkinson\u2019s disease detection based on keypress data. Biomedicines 10(11):2746","journal-title":"Biomedicines"},{"key":"3660_CR44","unstructured":"https:\/\/zenodo.org\/records\/7505822"},{"key":"3660_CR45","unstructured":"https:\/\/www.kaggle.com\/datasets\/linchundan\/fundusimage1000"}],"container-title":["International Ophthalmology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10792-025-03660-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10792-025-03660-w","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10792-025-03660-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T18:03:14Z","timestamp":1766167394000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10792-025-03660-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,5]]},"references-count":45,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["3660"],"URL":"https:\/\/doi.org\/10.1007\/s10792-025-03660-w","relation":{},"ISSN":["1573-2630"],"issn-type":[{"value":"1573-2630","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,5]]},"assertion":[{"value":"7 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 June 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 August 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 competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"324"}}