{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T18:43:32Z","timestamp":1771613012682,"version":"3.50.1"},"reference-count":53,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"Secretariat of Higher Education, Science, Technology and Innovation of the Republic of Ecuador"},{"DOI":"10.13039\/501100001871","name":"Portuguese research funding agency Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["UIDB\/EEA\/50008\/2020"],"award-info":[{"award-number":["UIDB\/EEA\/50008\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/access.2021.3095655","type":"journal-article","created":{"date-parts":[[2021,7,8]],"date-time":"2021-07-08T19:33:36Z","timestamp":1625772816000},"page":"99201-99219","source":"Crossref","is-referenced-by-count":29,"title":["Epiretinal Membrane Detection in Optical Coherence Tomography Retinal Images Using Deep Learning"],"prefix":"10.1109","volume":"9","author":[{"given":"Esther","family":"Parra-Mora","sequence":"first","affiliation":[]},{"given":"Alex","family":"Cazanas-Gordon","sequence":"additional","affiliation":[]},{"given":"Rui","family":"Proenca","sequence":"additional","affiliation":[]},{"given":"Luis A.","family":"da Silva Cruz","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","first-page":"1348","article-title":"Evaluating transfer learning for macular fluid detection with limited data","author":"cazanas-gordon","year":"2021","journal-title":"Proc 28th Eur Signal Process Conf (EUSIPCO)"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1007\/s12021-016-9318-5"},{"key":"ref33","article-title":"Deep residual learning for image recognition","author":"he","year":"2015","journal-title":"arXiv 1512 03385"},{"key":"ref32","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2014","journal-title":"arXiv 1409 1556"},{"key":"ref31","article-title":"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and & MB model size","author":"iandola","year":"2016","journal-title":"arXiv 1602 07360"},{"key":"ref30","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref37","first-page":"1166","article-title":"Towards Alzheimer’s disease classification through transfer learning","author":"hon","year":"2017","journal-title":"Proc IEEE Int Conf Bioinf Biomed (BIBM)"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2019.06.023"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.222"},{"key":"ref34","year":"2021","journal-title":"Evolution of CNN Architectures LeNet AlexNet ZFNet GoogleNet VGG and ResNet"},{"key":"ref28","year":"2021","journal-title":"Image"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2017.60"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref2","author":"mehta","year":"2021","journal-title":"Epiretinal Membrane—Eye Disorders"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/S0161-6420(02)01443-4"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-65405-2"},{"key":"ref22","author":"oh","year":"2021","journal-title":"Epiretinal Membrane—Medscape"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajo.2016.12.006"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1145\/2647868.2654970"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.2147\/OPTH.S97722"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-019-0276-2"},{"key":"ref25","doi-asserted-by":"crossref","DOI":"10.1088\/1742-6596\/1087\/6\/062018","article-title":"Evaluation and implementation of convolutional neural networks in image recognition","volume":"1087","author":"yi","year":"2018","journal-title":"J Phys Conf Ser"},{"key":"ref50","first-page":"249","article-title":"Understanding the difficulty of training deep feedforward neural networks","author":"glorot","year":"2010","journal-title":"Proc 13th Int Conf Artif Intell Statist"},{"key":"ref51","year":"2020","journal-title":"Scikit-learn Machine Learning in Python"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref52","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1007\/978-3-642-35289-8_26","article-title":"Practical recommendations for gradient-based training of deep architectures","author":"bengio","year":"2012","journal-title":"Neural Networks Tricks of the Trade"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2019.103445"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1364\/BOE.8.002732"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/6509357"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3076427"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2017.7950512"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.15439\/2018F127"},{"key":"ref15","first-page":"271","article-title":"Sine-Net: A fully convolutional deep learning architecture for retinal blood vessel segmentation","volume":"24","author":"atli","year":"2021","journal-title":"Eng Sci Technol Int J"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-59153-7_20"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1167\/tvst.7.6.41"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1364\/BOE.10.004018"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/s10792-018-1016-x"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.ophtha.2017.02.008"},{"key":"ref3","year":"2021","journal-title":"Epiretinal Membranes—The American Society of Retina Specialists"},{"key":"ref6","first-page":"147","article-title":"Automated detection of diabetic retinopathy using deep learning","volume":"2017","author":"lam","year":"2018","journal-title":"Amia Joint Summits Transl Sci"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2016.17216"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.ophtha.2017.10.031"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ICCP.2018.8516635"},{"key":"ref49","article-title":"An overview of gradient descent optimization algorithms","author":"ruder","year":"2016","journal-title":"arXiv 1609 04747"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1364\/BOE.8.003440"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1117\/1.JBO.22.6.066014"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1001\/jamaophthalmol.2018.1934"},{"key":"ref48","year":"2020","journal-title":"Pillow (pil fork)"},{"key":"ref47","year":"2020","journal-title":"PyTorch"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/5156416"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-017-09891-x","article-title":"Accuracy of deep learning, a machine-learning technology, using ultra–wide-field fundus ophthalmoscopy for detecting rhegmatogenous retinal detachment","volume":"7","author":"ohsugi","year":"2017","journal-title":"Sci Rep"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2960116"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9312710\/09477630.pdf?arnumber=9477630","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,27]],"date-time":"2022-01-27T11:36:56Z","timestamp":1643283416000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9477630\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":53,"URL":"https:\/\/doi.org\/10.1109\/access.2021.3095655","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]}}}