{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T18:05:33Z","timestamp":1774548333230,"version":"3.50.1"},"reference-count":57,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T00:00:00Z","timestamp":1770595200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T00:00:00Z","timestamp":1770595200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Int J Imaging Syst Tech"],"published-print":{"date-parts":[[2026,3]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>The purpose of this study is to develop a novel and effective deep learning method, named the Hybrid BeeHive Algorithm (HBA), for the intelligent identification and classification of retinal diseases using the Ocular Disease Intelligent Recognition (ODIR) dataset. The methodology involves the introduction of the HBA, which incorporates key elements from Inception, DenseNet, and VGG architectures. This study utilizes the ODIR dataset, consisting of 5000 cases from various Chinese hospitals, to train and evaluate the proposed model. The preprocessing phase includes standardizing image resolutions, normalizing pixel values, and augmenting data to enhance the model's generalizability. The HBA integrates data from color fundus photographs to create a robust multimodal system. Backpropagation is used to optimize the model's parameters, enhancing its ability to recognize complex patterns in the data. The results demonstrate the efficacy of the HBA in classifying various retinal diseases. The performance metrics for different models (DenseNet121, InceptionV3, and VGG19) are compared, highlighting their accuracy, F1 scores, precision, recall, and specificity in diagnosing age\u2010related macular degeneration (AMD) and other conditions. DenseNet121 and InceptionV3 models exhibit high performance, with InceptionV3 achieving near\u2010perfect metrics. Traditional machine learning models like RF and DT also show commendable performance but with some trade\u2010offs in recall. The HBA proves to be a significant advancement in the field of automated ocular disease diagnostics. Building upon existing multimodal ensemble approaches, HBA introduces a novel hybrid fusion strategy that integrates three deep CNN backbones for improved multi\u2010label retinal disease prediction.<\/jats:p>","DOI":"10.1002\/ima.70308","type":"journal-article","created":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T04:20:32Z","timestamp":1770697232000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Hybrid\n                    <scp>BeeHive<\/scp>\n                    Algorithm: Proposed Ensemble Model for Multiclass Multi\u2010Label Ophthalmological Eye Diseases Prediction"],"prefix":"10.1002","volume":"36","author":[{"given":"Akanksha","family":"Bali","sequence":"first","affiliation":[{"name":"Department of Computer Science and IT University of Jammu  Jammu Jammu &amp; Kashmir India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9654-1706","authenticated-orcid":false,"given":"Kuljeet","family":"Singh","sequence":"additional","affiliation":[{"name":"Chitkara University Institute of Engineering and Technology Chitkara University  Rajpura Punjab India"}]},{"given":"Vibhakar","family":"Mansotra","sequence":"additional","affiliation":[{"name":"Department of Computer Science and IT University of Jammu  Jammu Jammu &amp; Kashmir India"}]}],"member":"311","published-online":{"date-parts":[[2026,2,9]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/S2214-109X(17)30293-0"},{"issue":"10027","key":"e_1_2_11_3_1","first-page":"1748","article-title":"Diabetic Retinopathy","volume":"387","author":"Wong T. Y.","year":"2016","journal-title":"Lancet"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10384\u2010024\u2010XXXXX"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12889\u2010025\u201021573\u20102"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11831\u2010023\u201009989\u20108"},{"key":"e_1_2_11_7_1","first-page":"351","volume-title":"Proceedings of the 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N)","author":"Bali A.","year":"2021"},{"key":"e_1_2_11_8_1","first-page":"91","volume-title":"Proceedings of the 1st International Conference on Advances in Computing and Future Communication Technologies (ICACFCT)","author":"Bali A.","year":"2021"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2019.8759417"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.preteyeres.2018.07.004"},{"key":"e_1_2_11_11_1","first-page":"6105","volume-title":"Proceedings of the International Conference on Machine Learning (ICML)","author":"Tan M.","year":"2019"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"e_1_2_11_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-19-1122-4_8"},{"key":"e_1_2_11_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ophtha.2018.01.023"},{"key":"e_1_2_11_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10489\u2010024\u201006073\u2010X"},{"key":"e_1_2_11_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2024.3428349"},{"key":"e_1_2_11_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10637\u2010024\u201001464\u2010w"},{"key":"e_1_2_11_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"e_1_2_11_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"key":"e_1_2_11_21_1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1409.1556"},{"key":"e_1_2_11_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2020.102329"},{"key":"e_1_2_11_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3040275"},{"key":"e_1_2_11_24_1","first-page":"177","volume-title":"International Symposium on Benchmarking, Measuring and Optimization","author":"Li N.","year":"2020"},{"key":"e_1_2_11_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2020.102167"},{"key":"e_1_2_11_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICNC52316.2021.9608181"},{"key":"e_1_2_11_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.102491"},{"key":"e_1_2_11_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.106739"},{"key":"e_1_2_11_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105909"},{"key":"e_1_2_11_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106519"},{"key":"e_1_2_11_31_1","doi-asserted-by":"publisher","DOI":"10.22541\/au.167245202.20172177\/v1"},{"key":"e_1_2_11_32_1","doi-asserted-by":"publisher","DOI":"10.3233\/SHTI210537"},{"key":"e_1_2_11_33_1","unstructured":"Hanson0910 Pytorch\u2010RIADD: 1st Solution for Retinal Image Analysis for Multi\u2010Disease Detection Challenge (RIADD ISBI\u20102021)(2021) https:\/\/github.com\/Hanson0910\/Pytorch\u2010RIADD."},{"key":"e_1_2_11_34_1","first-page":"1","volume-title":"2022 International Conference on Electronics, Information, and Communication (ICEIC)","author":"Oh Y.\u2010T.","year":"2022"},{"key":"e_1_2_11_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.103768"},{"key":"e_1_2_11_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2022.3214086"},{"key":"e_1_2_11_37_1","doi-asserted-by":"publisher","DOI":"10.14569\/IJACSA.2021.0121269"},{"key":"e_1_2_11_38_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11042\u2010023\u201017530\u2010z"},{"key":"e_1_2_11_39_1","doi-asserted-by":"publisher","DOI":"10.3329\/jsr.v16i3.70620"},{"key":"e_1_2_11_40_1","doi-asserted-by":"publisher","DOI":"10.58491\/2735-4202.3214"},{"issue":"6","key":"e_1_2_11_41_1","first-page":"1123","article-title":"A Hybrid CNN\u2013RNN Approach for Multi\u2010Label Retinal Disease Classification","volume":"42","author":"Chen X.","year":"2023","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"e_1_2_11_42_1","article-title":"Multi\u2010Label Classification of Retinal Diseases Using Ensemble Learning With EfficientNet Variants","volume":"152","author":"Kumar R.","year":"2023","journal-title":"Computers in Biology and Medicine"},{"key":"e_1_2_11_43_1","article-title":"Enhancing CNN\u2010Based Retinal Disease Detection Using Contrast Adaptive Preprocessing","volume":"78","author":"Zhang T.","year":"2022","journal-title":"Biomedical Signal Processing and Control"},{"key":"e_1_2_11_44_1","first-page":"350","article-title":"Self\u2010Supervised Learning for Retinal Disease Detection Using Large\u2010Scale Unlabeled Datasets","volume":"167","author":"Lee J.","year":"2023","journal-title":"Neural Networks"},{"key":"e_1_2_11_45_1","article-title":"Hierarchical Attention Networks for Automated Multi\u2010Label Fundus Image Classification","volume":"81","author":"Han D.","year":"2023","journal-title":"Medical Image Analysis"},{"issue":"3","key":"e_1_2_11_46_1","first-page":"678","article-title":"Federated Learning for Privacy\u2010Preserving Multi\u2010Label Retinal Disease Classification","volume":"27","author":"Wang X.","year":"2023","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"key":"e_1_2_11_47_1","article-title":"Cross\u2010Domain Adaptation for Robust Retinal Disease Classification in Fundus Images","volume":"132","author":"Liu Y.","year":"2022","journal-title":"Pattern Recognition"},{"key":"e_1_2_11_48_1","article-title":"Hybrid CNN\u2013ViT Architecture for Improved Multi\u2010Label Classification of Ophthalmic Diseases","volume":"140","author":"Chen Y.","year":"2023","journal-title":"Artificial Intelligence in Medicine"},{"key":"e_1_2_11_49_1","article-title":"Weakly Supervised Learning for Multi\u2010Label Retinal Disease Classification","volume":"210","author":"Gao P.","year":"2022","journal-title":"Expert Systems With Applications"},{"key":"e_1_2_11_50_1","article-title":"Multi\u2010Label Retinal Disease Classification Using Graph Convolutional Networks","volume":"230","author":"Zhao J.","year":"2023","journal-title":"Computer Methods and Programs in Biomedicine"},{"key":"e_1_2_11_51_1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv:2207.07918"},{"key":"e_1_2_11_52_1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv:2207.02335"},{"key":"e_1_2_11_53_1","doi-asserted-by":"publisher","DOI":"10.3390\/mi13060947"},{"key":"e_1_2_11_54_1","doi-asserted-by":"publisher","DOI":"10.3390\/app132011437"},{"key":"e_1_2_11_55_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-024-02358-2"},{"key":"e_1_2_11_56_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bios.2025.117408"},{"key":"e_1_2_11_57_1","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbac072"},{"key":"e_1_2_11_58_1","doi-asserted-by":"publisher","DOI":"10.2174\/1574893618666230504143647"}],"container-title":["International Journal of Imaging Systems and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/ima.70308","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/ima.70308","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/ima.70308","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T17:13:00Z","timestamp":1774545180000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/ima.70308"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,9]]},"references-count":57,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,3]]}},"alternative-id":["10.1002\/ima.70308"],"URL":"https:\/\/doi.org\/10.1002\/ima.70308","archive":["Portico"],"relation":{},"ISSN":["0899-9457","1098-1098"],"issn-type":[{"value":"0899-9457","type":"print"},{"value":"1098-1098","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,9]]},"assertion":[{"value":"2025-03-28","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-01-24","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-02-09","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70308"}}