{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T15:15:07Z","timestamp":1759331707811,"version":"3.44.0"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"32","license":[{"start":{"date-parts":[[2025,3,22]],"date-time":"2025-03-22T00:00:00Z","timestamp":1742601600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,3,22]],"date-time":"2025-03-22T00:00:00Z","timestamp":1742601600000},"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":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-025-20764-8","type":"journal-article","created":{"date-parts":[[2025,3,23]],"date-time":"2025-03-23T03:22:28Z","timestamp":1742700148000},"page":"40343-40361","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Imbalanced multilabel retinal disease classification using threshold moving and ensemble learning"],"prefix":"10.1007","volume":"84","author":[{"given":"Gaurav","family":"Pendharkar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sudharshanan","family":"Balaji","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"B. Muhesh","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"G.","family":"Malathi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,3,22]]},"reference":[{"issue":"4","key":"20764_CR1","doi-asserted-by":"publisher","first-page":"3594","DOI":"10.3390\/ijerph20043594","volume":"20","author":"A Kami\u0144ska","year":"2023","unstructured":"Kami\u0144ska A, Pinkas J, Wrze\u015bniewska-Wal I, Ostrowski J, Jankowski M (2023) Awareness of Common Eye Diseases and Their Risk Factors\u2014A Nationwide Cross-Sectional Survey among Adults in Poland. Int J Environ Res Public Health 20(4):3594","journal-title":"Int J Environ Res Public Health"},{"key":"20764_CR2","unstructured":"Shukla UV, Tripathy K (2023) Diabetic retinopathy. In: StatPearls. StatPearls Publishing. https:\/\/www.ncbi.nlm.nih.gov\/books\/NBK560805\/. Accessed 16 May 2023"},{"key":"20764_CR3","unstructured":"Majumdar S, Tripathy K (2023) Macular hole. In: StatPearls. StatPearls Publishing. https:\/\/www.ncbi.nlm.nih.gov\/books\/NBK559200\/. Accessed 16 May 2023"},{"key":"20764_CR4","doi-asserted-by":"crossref","unstructured":"Pachade S, Porwal P, Thulkar D, Kokare M, Deshmukh G, Sahasrabuddhe V, ... M\u00e9riaudeau F (2021) Retinal fundus multi-disease image dataset (RFMiD): a dataset for multi-disease detection research. Data, 6(2), 14","DOI":"10.3390\/data6020014"},{"key":"20764_CR5","unstructured":"Read J, Perez-Cruz F (2014) Deep learning for multi-label classification. arXiv preprint arXiv:1502.05988"},{"key":"20764_CR6","doi-asserted-by":"crossref","unstructured":"Zhang X, Gweon H, Provost S (2020) Threshold moving approaches for addressing the class imbalance problem and their application to multi-label classification. In: Proceedings of the 4th International Conference on Advances in Image Processing, pp 72\u201377","DOI":"10.1145\/3441250.3441274"},{"issue":"10","key":"20764_CR7","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1167\/tvst.11.10.39","volume":"11","author":"E Ho","year":"2022","unstructured":"Ho E, Wang E, Youn S, Sivajohan A, Lane K, Chun J, Hutnik CM (2022) Deep Ensemble Learning for Retinal Image Classification. Transl Vis Sci Technol 11(10):39\u201339","journal-title":"Transl Vis Sci Technol"},{"key":"20764_CR8","doi-asserted-by":"crossref","unstructured":"M\u00fcller D, Soto-Rey I, Kramer F (2021) Multi-disease detection in retinal imaging based on ensembling heterogeneous deep learning models. In: German medical data sciences 2021: digital medicine: recognize\u2013understand\u2013heal, pp 23\u201331. IOS Press","DOI":"10.3233\/SHTI210537"},{"key":"20764_CR9","doi-asserted-by":"crossref","unstructured":"Casado-Garc\u00eda \u00c1, Garc\u00eda-Dom\u00ednguez M, Heras J, In\u00e9s A, Royo D, Zapata M\u00c1 (2021) Prediction of epiretinal membrane from retinal fundus images using deep learning. In: Advances in artificial intelligence: 19th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2020\/2021, M\u00e1laga, Spain, September 22\u201324, 2021, Proceedings 19, pp 3\u201313. Springer International Publishing","DOI":"10.1007\/978-3-030-85713-4_1"},{"key":"20764_CR10","doi-asserted-by":"crossref","unstructured":"Bragan\u00e7a CP, Torres JM, Soares CPDA, Macedo LO (2022) Detection of glaucoma on fundus images using deep learning on a new image set obtained with a smartphone and handheld ophthalmoscope. In Healthcare (Vol 10, No 12, p 2345). MDPI","DOI":"10.3390\/healthcare10122345"},{"key":"20764_CR11","doi-asserted-by":"crossref","unstructured":"Tufail AB, Ullah I, Khan WU, Asif M, Ahmad I, Ma YK, ... Ali MS (2021) Diagnosis of diabetic retinopathy through retinal fundus images and 3D convolutional neural networks with limited number of samples. Wireless Communications and Mobile Computing, 2021, 1\u201315","DOI":"10.1155\/2021\/6013448"},{"issue":"13","key":"20764_CR12","doi-asserted-by":"publisher","first-page":"1966","DOI":"10.3390\/electronics11131966","volume":"11","author":"O Ouda","year":"2022","unstructured":"Ouda O, AbdelMaksoud E, Abd El-Aziz AA, Elmogy M (2022) Multiple ocular disease diagnosis using fundus images based on multi-label deep learning classification. Electronics 11(13):1966","journal-title":"Electronics"},{"key":"20764_CR13","doi-asserted-by":"crossref","unstructured":"Lumbantoruan AA, Bustamam A, Anki P (2021) Retinal disease for clasification multilabel with applying convolutional neural networks based support vector machine and DenseNet. In: 2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) (pp. 475\u2013479). IEEE","DOI":"10.1109\/ISRITI54043.2021.9702861"},{"key":"20764_CR14","doi-asserted-by":"crossref","unstructured":"Rodr\u00edguez MA, AlMarzouqi H, Liatsis P (2022) Multi-label retinal disease classification using transformers. IEEE J Biomed Health Inform","DOI":"10.1109\/JBHI.2022.3214086"},{"key":"20764_CR15","unstructured":"Abbas R, Gilani SO, Waris A. Ensemble based multi-retinal disease classification and application with Rfmid dataset using deep learning"},{"key":"20764_CR16","doi-asserted-by":"crossref","unstructured":"Lydia AA, Francis FS (2020) Multi-label classification using deep convolutional neural network. In: 2020 international conference on innovative trends in information technology (ICITIIT), pp 1\u20136. IEEE","DOI":"10.1109\/ICITIIT49094.2020.9071539"},{"issue":"1","key":"20764_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-019-0197-0","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten C, Khoshgoftaar TM (2019) A survey on image data augmentation for deep learning. J Big Data 6(1):1\u201348","journal-title":"J Big Data"},{"key":"20764_CR18","unstructured":"Tan M, Le QV (2020) Efficientnet: rethinking model scaling for convolutional neural networks. arXiv 2019. arXiv preprint arXiv:1905.11946"},{"key":"20764_CR19","doi-asserted-by":"crossref","unstructured":"Zoph B, Vasudevan V, Shlens J, Le QV (2018) Learning transferable architectures for scalable image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 8697\u20138710","DOI":"10.1109\/CVPR.2018.00907"},{"key":"20764_CR20","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556"},{"key":"20764_CR21","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4700\u20134708","DOI":"10.1109\/CVPR.2017.243"},{"key":"20764_CR22","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"20764_CR23","doi-asserted-by":"publisher","first-page":"99129","DOI":"10.1109\/ACCESS.2022.3207287","volume":"10","author":"ID Mienye","year":"2022","unstructured":"Mienye ID, Sun Y (2022) A survey of ensemble learning: Concepts, algorithms, applications, and prospects. IEEE Access 10:99129\u201399149","journal-title":"IEEE Access"},{"issue":"7","key":"20764_CR24","doi-asserted-by":"publisher","first-page":"1145","DOI":"10.1016\/S0031-3203(96)00142-2","volume":"30","author":"AP Bradley","year":"1997","unstructured":"Bradley AP (1997) The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern Recognit 30(7):1145\u20131159. https:\/\/doi.org\/10.1016\/S0031-3203(96)00142-2","journal-title":"Pattern Recognit"},{"issue":"1","key":"20764_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/cc3045","volume":"9","author":"V Bewick","year":"2005","unstructured":"Bewick V, Cheek L, Ball J (2005) Statistics review 14: Logistic regression. Crit Care 9(1):1\u20137","journal-title":"Crit Care"},{"key":"20764_CR26","doi-asserted-by":"crossref","unstructured":"Boser BE, Guyon IM, Vapnik VN (1992). A training algorithm for optimal margin classifiers. In: Proceedings of the fifth annual workshop on Computational learning theory, pp 144\u2013152","DOI":"10.1145\/130385.130401"},{"key":"20764_CR27","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1023\/A:1022643204877","volume":"1","author":"JR Quinlan","year":"1986","unstructured":"Quinlan JR (1986) Induction of decision trees. Mach Learn 1:81\u2013106","journal-title":"Mach Learn"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-025-20764-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-025-20764-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-025-20764-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T15:13:45Z","timestamp":1758899625000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-025-20764-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,22]]},"references-count":27,"journal-issue":{"issue":"32","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["20764"],"URL":"https:\/\/doi.org\/10.1007\/s11042-025-20764-8","relation":{},"ISSN":["1573-7721"],"issn-type":[{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2025,3,22]]},"assertion":[{"value":"14 August 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 November 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 March 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 March 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":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interests"}}]}}