{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T08:59:22Z","timestamp":1782377962859,"version":"3.54.5"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,6,13]],"date-time":"2022-06-13T00:00:00Z","timestamp":1655078400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,6,13]],"date-time":"2022-06-13T00:00:00Z","timestamp":1655078400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Big Data"],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Because retinal hemorrhage is one of the earliest symptoms of diabetic retinopathy, its accurate identification is essential for early diagnosis. One of the major obstacles ophthalmologists face in making a quick and effective diagnosis is viewing too many images to manually identify lesions of different shapes and sizes. To this end, researchers are working to develop an automated method for screening for diabetic retinopathy. This paper presents a modified CNN UNet architecture for identifying retinal hemorrhages in fundus images. Using the graphics processing unit (GPU) and the IDRiD dataset, the proposed UNet was trained to segment and detect potential areas that may harbor retinal hemorrhages. The experiment was also tested using the IDRiD and DIARETDB1 datasets, both freely available on the Internet. We applied preprocessing to improve the image quality and increase the data, which play an important role in defining the complex features involved in the segmentation task. A significant improvement was then observed in the learning neural network that was able to effectively segment the bleeding and achieve sensitivity, specificity and accuracy of 80.49%, 99.68%, and 98.68%, respectively. The experimental results also yielded an IoU of 76.61% and a Dice value of 86.51%, showing that the predictions obtained by the network are effective and can significantly reduce the efforts of ophthalmologists. The results revealed a significant increase in the diagnostic performance of one of the most important retinal disorders caused by diabetes.<\/jats:p>","DOI":"10.1186\/s40537-022-00632-0","type":"journal-article","created":{"date-parts":[[2022,6,13]],"date-time":"2022-06-13T19:30:37Z","timestamp":1655148637000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":64,"title":["Hemorrhage semantic segmentation in fundus images for the diagnosis of diabetic retinopathy by using a convolutional neural network"],"prefix":"10.1186","volume":"9","author":[{"given":"Ayoub","family":"Skouta","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdelali","family":"Elmoufidi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Said","family":"Jai-Andaloussi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ouail","family":"Ouchetto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,6,13]]},"reference":[{"key":"632_CR1","doi-asserted-by":"publisher","first-page":"2789","DOI":"10.2147\/DMSO.S312787","volume":"14","author":"P Nagaraj","year":"2021","unstructured":"Nagaraj P, Deepalakshmi P, Romany FM. Artificial flora algorithm-based feature selection with gradient boosted tree model for diabetes classification. Diabetes Metab Syndrome Obes Targets Ther. 2021;14:2789.","journal-title":"Diabetes Metab Syndrome Obes Targets Ther"},{"key":"632_CR2","first-page":"1927","volume":"12","author":"Y-H Zhang","year":"2021","unstructured":"Zhang Y-H, Guo W, Zeng T, Zhang S, Chen L, Gamarra M, Mansour RF, Escorcia-Gutierrez J, Huang T, Cai Y-D. Identification of microbiota biomarkers with orthologous gene annotation for type 2 diabetes. Front Microbiol. 2021;12:1927.","journal-title":"Front Microbiol"},{"key":"632_CR3","first-page":"1097","volume":"25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst. 2012;25:1097\u2013105.","journal-title":"Adv Neural Inf Process Syst"},{"key":"632_CR4","doi-asserted-by":"publisher","DOI":"10.1142\/S0219467823500122","author":"A Elmoufidi","year":"2022","unstructured":"Elmoufidi A, Skouta A, Jai-Andaloussi S, Ouchetto O. CNN with multiple inputs for automatic glaucoma assessment using fundus images. Int J Image Graph. 2022. https:\/\/doi.org\/10.1142\/S0219467823500122.","journal-title":"Int J Image Graph"},{"key":"632_CR5","volume-title":"Pathologies vasculaires oculaires","author":"CJ Pournaras","year":"2008","unstructured":"Pournaras CJ. Pathologies vasculaires oculaires. Issy-les-Moulineaux: Elsevier Masson; 2008."},{"key":"632_CR6","doi-asserted-by":"publisher","DOI":"10.1155\/2016\/6838976","author":"J Amin","year":"2016","unstructured":"Amin J, Sharif M, Yasmin M. A review on recent developments for detection of diabetic retinopathy. Scientifica. 2016. https:\/\/doi.org\/10.1155\/2016\/6838976.","journal-title":"Scientifica"},{"key":"632_CR7","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1109\/RBME.2017.2705064","volume":"10","author":"RF Mansour","year":"2017","unstructured":"Mansour RF. Evolutionary computing enriched computer-aided diagnosis system for diabetic retinopathy: a survey. IEEE Rev Biomed Eng. 2017;10:334\u201349.","journal-title":"IEEE Rev Biomed Eng"},{"issue":"4","key":"632_CR8","doi-asserted-by":"publisher","first-page":"10845","DOI":"10.1016\/j.matpr.2017.12.372","volume":"5","author":"D Devaraj","year":"2018","unstructured":"Devaraj D, Suma R, Kumar SP. A survey on segmentation of exudates and microaneurysms for early detection of diabetic retinopathy. Mater Today Proc. 2018;5(4):10845\u201350.","journal-title":"Mater Today Proc"},{"issue":"10","key":"632_CR9","doi-asserted-by":"publisher","first-page":"5042","DOI":"10.1364\/BOE.10.005042","volume":"10","author":"Y He","year":"2019","unstructured":"He Y, Carass A, Liu Y, Jedynak BM, Solomon SD, Saidha S, Calabresi PA, Prince JL. Deep learning based topology guaranteed surface and MME segmentation of multiple sclerosis subjects from retinal OCT. Biomed Opt Express. 2019;10(10):5042\u201358.","journal-title":"Biomed Opt Express"},{"key":"632_CR10","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1016\/j.compeleceng.2019.03.004","volume":"76","author":"T Shanthi","year":"2019","unstructured":"Shanthi T, Sabeenian R. Modified Alexnet architecture for classification of diabetic retinopathy images. Comput Electr Eng. 2019;76:56\u201364.","journal-title":"Comput Electr Eng"},{"key":"632_CR11","doi-asserted-by":"crossref","unstructured":"Skouta A, Elmoufidi A, Jai-Andaloussi S, Ochetto O. Automated binary classification of diabetic retinopathy by convolutional neural networks. In: Advances on smart and soft computing. Singapore: Springer; 2021. p. 177\u201387.","DOI":"10.1007\/978-981-15-6048-4_16"},{"key":"632_CR12","doi-asserted-by":"crossref","unstructured":"El Hossi A, Skouta A, Elmoufidi A, Nachaoui M. Applied CNN for automatic diabetic retinopathy assessment using fundus images. In: International conference on business intelligence. Springer; 2021. p. 425\u201333.","DOI":"10.1007\/978-3-030-76508-8_31"},{"issue":"5","key":"632_CR13","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1109\/LGRS.2018.2802944","volume":"15","author":"Z Zhang","year":"2018","unstructured":"Zhang Z, Liu Q, Wang Y. Road extraction by deep residual U-Net. IEEE Geosci Remote Sens Lett. 2018;15(5):749\u201353.","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"632_CR14","doi-asserted-by":"crossref","unstructured":"Noori M, Bahri A, Mohammadi K. Attention-guided version of 2D UNet for automatic brain tumor segmentation. In: 2019 9th international conference on computer and knowledge engineering (ICCKE). IEEE; 2019. p. 269\u201375.","DOI":"10.1109\/ICCKE48569.2019.8964956"},{"key":"632_CR15","doi-asserted-by":"crossref","unstructured":"Girshick R, Donahue J, Darrell T, Malik J. Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2014. p. 580\u20137.","DOI":"10.1109\/CVPR.2014.81"},{"issue":"3","key":"632_CR16","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1049\/iet-ipr.2017.0536","volume":"12","author":"A Elmoufidi","year":"2018","unstructured":"Elmoufidi A, El Fahssi K, Jai-Andaloussi S, Sekkaki A, Gwenole Q, Lamard M. Anomaly classification in digital mammography based on multiple-instance learning. IET Image Process. 2018;12(3):320\u20138.","journal-title":"IET Image Process"},{"key":"632_CR17","doi-asserted-by":"crossref","unstructured":"Elmoufidi A, El\u00a0Fahssi K, Jai-Andaloussi S, Madrane N, Sekkaki A. Detection of regions of interest\u2019s in mammograms by using local binary pattern, dynamic k-means algorithm and gray level co-occurrence matrix. In: 2014 international conference on next generation networks and services (NGNS). IEEE; 2014. p. 118\u201323.","DOI":"10.1109\/NGNS.2014.6990239"},{"key":"632_CR18","doi-asserted-by":"crossref","unstructured":"Jai-Andaloussi S, Sekkaki A, Quellec G, Lamard M, Cazuguel G, Roux C. Mass segmentation in mammograms by using bidimensional emperical mode decomposition BEMD. In: 2013 35th annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE; 2013. p. 5441\u20134.","DOI":"10.1109\/EMBC.2013.6610780"},{"issue":"1","key":"632_CR19","first-page":"2336-0992","volume":"1","author":"A Elmoufidi","year":"2014","unstructured":"Elmoufidi A, El Fahssi K, Jai-Andaloussi S, Sekkaki A. Detection of regions of interest in mammograms by using local binary pattern and dynamic K-means algorithm. Int J Image Video Process Theory Appl. 2014;1(1):2336\u20130992.","journal-title":"Int J Image Video Process Theory Appl"},{"key":"632_CR20","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-021-03613-y","author":"S Jafarzadeh Ghoushchi","year":"2021","unstructured":"Jafarzadeh Ghoushchi S, Ranjbarzadeh R, Najafabadi SA, Osgooei E, Tirkolaee EB. An extended approach to the diagnosis of tumour location in breast cancer using deep learning. J Ambient Intell Hum Comput. 2021. https:\/\/doi.org\/10.1007\/s12652-021-03613-y.","journal-title":"J Ambient Intell Hum Comput"},{"issue":"11","key":"632_CR21","doi-asserted-by":"publisher","first-page":"5526","DOI":"10.1016\/j.eswa.2014.01.021","volume":"41","author":"E-SA El-Dahshan","year":"2014","unstructured":"El-Dahshan E-SA, Mohsen HM, Revett K, Salem A-BM. Computer-aided diagnosis of human brain tumor through MRI: a survey and a new algorithm. Expert Syst Appl. 2014;41(11):5526\u201345.","journal-title":"Expert Syst Appl"},{"issue":"1","key":"632_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-021-90428-8","volume":"11","author":"R Ranjbarzadeh","year":"2021","unstructured":"Ranjbarzadeh R, Bagherian Kasgari A, Jafarzadeh Ghoushchi S, Anari S, Naseri M, Bendechache M. Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images. Sci Rep. 2021;11(1):1\u201317.","journal-title":"Sci Rep"},{"issue":"22","key":"632_CR23","doi-asserted-by":"publisher","first-page":"2402","DOI":"10.1001\/jama.2016.17216","volume":"316","author":"V Gulshan","year":"2016","unstructured":"Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316(22):2402\u201310.","journal-title":"JAMA"},{"key":"632_CR24","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/5597222","author":"SJ Ghoushchi","year":"2021","unstructured":"Ghoushchi SJ, Ranjbarzadeh R, Dadkhah AH, Pourasad Y, Bendechache M. An extended approach to predict retinopathy in diabetic patients using the genetic algorithm and fuzzy C-means. BioMed Res Int. 2021. https:\/\/doi.org\/10.1155\/2021\/5597222.","journal-title":"BioMed Res Int"},{"issue":"11","key":"632_CR25","doi-asserted-by":"publisher","first-page":"940","DOI":"10.1136\/bjo.80.11.940","volume":"80","author":"GG Gardner","year":"1996","unstructured":"Gardner GG, Keating D, Williamson TH, Elliott AT. Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool. Br J Ophthalmol. 1996;80(11):940\u20134.","journal-title":"Br J Ophthalmol"},{"issue":"2","key":"632_CR26","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1046\/j.1464-5491.2002.00613.x","volume":"19","author":"C Sinthanayothin","year":"2002","unstructured":"Sinthanayothin C, Boyce JF, Williamson TH, Cook HL, Mensah E, Lal S, Usher D. Automated detection of diabetic retinopathy on digital fundus images. Diabet Med. 2002;19(2):105\u201312.","journal-title":"Diabet Med"},{"issue":"4","key":"632_CR27","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1007\/s10278-009-9246-0","volume":"23","author":"GB Kande","year":"2010","unstructured":"Kande GB, Savithri TS, Subbaiah PV. Automatic detection of microaneurysms and hemorrhages in digital fundus images. J Digit Imaging. 2010;23(4):430\u20137.","journal-title":"J Digit Imaging"},{"issue":"2","key":"632_CR28","doi-asserted-by":"publisher","first-page":"364","DOI":"10.1109\/TMI.2012.2227119","volume":"32","author":"L Tang","year":"2012","unstructured":"Tang L, Niemeijer M, Reinhardt JM, Garvin MK, Abramoff MD. Splat feature classification with application to retinal hemorrhage detection in fundus images. IEEE Trans Med Imaging. 2012;32(2):364\u201375.","journal-title":"IEEE Trans Med Imaging"},{"issue":"5","key":"632_CR29","doi-asserted-by":"publisher","first-page":"1273","DOI":"10.1109\/TMI.2016.2526689","volume":"35","author":"MJ Van Grinsven","year":"2016","unstructured":"Van Grinsven MJ, van Ginneken B, Hoyng CB, Theelen T, S\u00e1nchez CI. Fast convolutional neural network training using selective data sampling: application to hemorrhage detection in color fundus images. IEEE Trans Med Imaging. 2016;35(5):1273\u201384.","journal-title":"IEEE Trans Med Imaging"},{"key":"632_CR30","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.ins.2017.08.050","volume":"420","author":"JH Tan","year":"2017","unstructured":"Tan JH, Fujita H, Sivaprasad S, Bhandary SV, Rao AK, Chua KC, Acharya UR. Automated segmentation of exudates, haemorrhages, microaneurysms using single convolutional neural network. Inf Sci. 2017;420:66\u201376.","journal-title":"Inf Sci"},{"key":"632_CR31","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1016\/j.media.2017.04.012","volume":"39","author":"G Quellec","year":"2017","unstructured":"Quellec G, Charri\u00e8re K, Boudi Y, Cochener B, Lamard M. Deep image mining for diabetic retinopathy screening. Med Image Anal. 2017;39:178\u201393.","journal-title":"Med Image Anal"},{"key":"632_CR32","doi-asserted-by":"publisher","DOI":"10.4066\/biomedicalresearch.29-17-820","author":"S Karkuzhali","year":"2018","unstructured":"Karkuzhali S, Manimegalai D. Retinal haemorrhages segmentation using improved toboggan segmentation algorithm in diabetic retinopathy images. Biomed Res. 2018. https:\/\/doi.org\/10.4066\/biomedicalresearch.29-17-820.","journal-title":"Biomed Res"},{"issue":"1","key":"632_CR33","doi-asserted-by":"publisher","first-page":"590","DOI":"10.1167\/iovs.17-22721","volume":"59","author":"C Lam","year":"2018","unstructured":"Lam C, Yu C, Huang L, Rubin D. Retinal lesion detection with deep learning using image patches. Investig Ophthalmol Vis Sci. 2018;59(1):590\u20136.","journal-title":"Investig Ophthalmol Vis Sci"},{"key":"632_CR34","doi-asserted-by":"crossref","unstructured":"Badar M, Shahzad M, Fraz M. Simultaneous segmentation of multiple retinal pathologies using fully convolutional deep neural network. In: Annual conference on medical image understanding and analysis. Springer; 2018. p. 313\u201324.","DOI":"10.1007\/978-3-319-95921-4_29"},{"key":"632_CR35","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.cmpb.2017.10.017","volume":"153","author":"JI Orlando","year":"2018","unstructured":"Orlando JI, Prokofyeva E, Del Fresno M, Blaschko MB. An ensemble deep learning based approach for red lesion detection in fundus images. Comput Methods Programs Biomed. 2018;153:115\u201327.","journal-title":"Comput Methods Programs Biomed"},{"key":"632_CR36","unstructured":"Saha O, Sathish R, Sheet D. Fully convolutional neural network for semantic segmentation of anatomical structure and pathologies in colour fundus images associated with diabetic retinopathy. arXiv preprint. 2019. arXiv:1902.03122."},{"key":"632_CR37","doi-asserted-by":"crossref","unstructured":"Ananda S, Kitahara D, Hirabayashi A, Reddy KUK. Automatic fundus image segmentation for diabetic retinopathy diagnosis by multiple modified U-Nets and SegNets. In: 2019 Asia-Pacific signal and information processing association annual summit and conference (APSIPA ASC). IEEE; 2019. p. 1582\u20138.","DOI":"10.1109\/APSIPAASC47483.2019.9023290"},{"key":"632_CR38","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.neucom.2019.04.019","volume":"349","author":"S Guo","year":"2019","unstructured":"Guo S, Li T, Kang H, Li N, Zhang Y, Wang K. L-Seg: an end-to-end unified framework for multi-lesion segmentation of fundus images. Neurocomputing. 2019;349:52\u201363.","journal-title":"Neurocomputing"},{"key":"632_CR39","doi-asserted-by":"crossref","unstructured":"Yan Z, Han X, Wang C, Qiu Y, Xiong Z, Cui S. Learning mutually local-global u-nets for high-resolution retinal lesion segmentation in fundus images. In: 2019 IEEE 16th international symposium on biomedical imaging (ISBI 2019). IEEE; 2019. p. 597\u2013600.","DOI":"10.1109\/ISBI.2019.8759579"},{"key":"632_CR40","doi-asserted-by":"crossref","unstructured":"Huang Y, Lin L, Li M, Wu J, Cheng P, Wang K, Yuan J, Tang X. Automated hemorrhage detection from coarsely annotated fundus images in diabetic retinopathy. In: 2020 IEEE 17th international symposium on biomedical imaging (ISBI). IEEE; 2020. p. 1369\u201372.","DOI":"10.1109\/ISBI45749.2020.9098319"},{"key":"632_CR41","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A. Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2015. p. 1\u20139.","DOI":"10.1109\/CVPR.2015.7298594"},{"issue":"3","key":"632_CR42","doi-asserted-by":"publisher","first-page":"1094","DOI":"10.1016\/j.bbe.2020.05.006","volume":"40","author":"N Sambyal","year":"2020","unstructured":"Sambyal N, Saini P, Syal R, Gupta V. Modified U-Net architecture for semantic segmentation of diabetic retinopathy images. Biocybern Biomed Eng. 2020;40(3):1094\u2013109.","journal-title":"Biocybern Biomed Eng"},{"key":"632_CR43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.preteyeres.2018.07.004","volume":"67","author":"U Schmidt-Erfurth","year":"2018","unstructured":"Schmidt-Erfurth U, Sadeghipour A, Gerendas BS, Waldstein SM, Bogunovi\u0107 H. Artificial intelligence in retina. Prog Retinal Eye Res. 2018;67:1\u201329.","journal-title":"Prog Retinal Eye Res"},{"key":"632_CR44","volume-title":"Advanced deep learning with TensorFlow 2 and Keras: apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more","author":"R Atienza","year":"2020","unstructured":"Atienza R. Advanced deep learning with TensorFlow 2 and Keras: apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more. Birmingham: Packt Publishing Ltd; 2020."},{"issue":"1","key":"632_CR45","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1113\/jphysiol.1962.sp006837","volume":"160","author":"DH Hubel","year":"1962","unstructured":"Hubel DH, Wiesel TN. Receptive fields, binocular interaction and functional architecture in the cat\u2019s visual cortex. J Physiol. 1962;160(1):106\u201354.","journal-title":"J Physiol"},{"key":"632_CR46","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/5544742","author":"R Ranjbarzadeh","year":"2021","unstructured":"Ranjbarzadeh R, Jafarzadeh Ghoushchi S, Bendechache M, Amirabadi A, Ab Rahman MN, Baseri Saadi S, Aghamohammadi A, Kooshki Forooshani M. Lung infection segmentation for COVID-19 pneumonia based on a cascade convolutional network from CT images. BioMed Res Int. 2021. https:\/\/doi.org\/10.1155\/2021\/5544742.","journal-title":"BioMed Res Int"},{"key":"632_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2019.100203","volume":"35","author":"M Badar","year":"2020","unstructured":"Badar M, Haris M, Fatima A. Application of deep learning for retinal image analysis: a review. Comput Sci Rev. 2020;35: 100203.","journal-title":"Comput Sci Rev"},{"issue":"1","key":"632_CR48","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1007\/s13534-017-0047-y","volume":"8","author":"RF Mansour","year":"2018","unstructured":"Mansour RF. Deep-learning-based automatic computer-aided diagnosis system for diabetic retinopathy. Biomed Eng Lett. 2018;8(1):41\u201357.","journal-title":"Biomed Eng Lett"},{"key":"632_CR49","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer: Cham; 2015. p. 234\u201341.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"632_CR50","doi-asserted-by":"publisher","first-page":"122634","DOI":"10.1109\/ACCESS.2019.2935138","volume":"7","author":"P Xiuqin","year":"2019","unstructured":"Xiuqin P, Zhang Q, Zhang H, Li S. A fundus retinal vessels segmentation scheme based on the improved deep learning U-Net model. IEEE Access. 2019;7:122634\u201343.","journal-title":"IEEE Access"},{"key":"632_CR51","doi-asserted-by":"crossref","unstructured":"Skouta A, Elmoufidi A, Jai-Andaloussi S, Ouchetto, O. Semantic segmentation of retinal blood vessels from fundus images by using CNN and the random forest algorithm; 2022.","DOI":"10.5220\/0010911800003118"},{"issue":"3","key":"632_CR52","doi-asserted-by":"publisher","first-page":"25","DOI":"10.3390\/data3030025","volume":"3","author":"P Porwal","year":"2018","unstructured":"Porwal P, Pachade S, Kamble R, Kokare M, Deshmukh G, Sahasrabuddhe V, Meriaudeau F. Indian diabetic retinopathy image dataset (IDRiD): a database for diabetic retinopathy screening research. Data. 2018;3(3):25.","journal-title":"Data"},{"key":"632_CR53","doi-asserted-by":"crossref","unstructured":"Kauppi T, Kalesnykiene V, Kamarainen J-K, Lensu L, Sorri I, Raninen A, Voutilainen R, Uusitalo H, K\u00e4lvi\u00e4inen H, Pietil\u00e4 J. The diaretdb1 diabetic retinopathy database and evaluation protocol. In: BMVC, vol. 1. 2007. p. 1\u201310.","DOI":"10.5244\/C.21.15"},{"key":"632_CR54","unstructured":"Kingma DP, Ba J. Adam: a method for stochastic optimization. arXiv preprint. 2014. arXiv:1412.6980."}],"container-title":["Journal of Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-022-00632-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40537-022-00632-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-022-00632-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,13]],"date-time":"2022-06-13T19:33:14Z","timestamp":1655148794000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalofbigdata.springeropen.com\/articles\/10.1186\/s40537-022-00632-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,13]]},"references-count":54,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["632"],"URL":"https:\/\/doi.org\/10.1186\/s40537-022-00632-0","relation":{},"ISSN":["2196-1115"],"issn-type":[{"value":"2196-1115","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,13]]},"assertion":[{"value":"4 March 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 May 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 June 2022","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 accept the Journal of Big Data\u2019s ethics approval and agree to share their work for scientific advancement.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The authors hereby consent to the publication of the work in the Journal of Big Data.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors disclose that they do not have any competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"78"}}