{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T13:48:02Z","timestamp":1782481682664,"version":"3.54.5"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2022,10,24]],"date-time":"2022-10-24T00:00:00Z","timestamp":1666569600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,10,24]],"date-time":"2022-10-24T00:00:00Z","timestamp":1666569600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2022,12]]},"DOI":"10.1007\/s11517-022-02688-9","type":"journal-article","created":{"date-parts":[[2022,10,24]],"date-time":"2022-10-24T03:21:25Z","timestamp":1666581685000},"page":"3635-3654","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["An automated unsupervised deep learning\u2013based approach for diabetic retinopathy detection"],"prefix":"10.1007","volume":"60","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4393-5378","authenticated-orcid":false,"given":"Huma","family":"Naz","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rahul","family":"Nijhawan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Neelu Jyothi","family":"Ahuja","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,10,24]]},"reference":[{"issue":"1","key":"2688_CR1","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1002\/jbt.10058","volume":"17","author":"AC Maritim","year":"2003","unstructured":"Maritim AC, Sanders RA, Watkins JB (2003) Diabetes, oxidative stress, and antioxidants: a review. J Biochem Mol Toxicol 17(1):24\u201338. https:\/\/doi.org\/10.1002\/jbt.10058","journal-title":"J Biochem Mol Toxicol"},{"key":"2688_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/IWAIT.2018.8369794","volume":"2018","author":"Y Hatanaka","year":"2018","unstructured":"Hatanaka Y, Ogohara K, Sunayama W, Miyashita M, Muramatsu C, Fujita H (2018) Automatic microaneurysms detection on retinal images using deep convolution neural network. 2018 International Workshop on Advanced Image Technology IWAIT 2018:1\u20132. https:\/\/doi.org\/10.1109\/IWAIT.2018.8369794","journal-title":"2018 International Workshop on Advanced Image Technology IWAIT"},{"key":"2688_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2021.105992","volume":"205","author":"A AlAkeely","year":"2021","unstructured":"AlAkeely A, Alsubaie H, Alharbi A, Alsaawi F, Alshabib L, Albahlal A, Alzeer A (2021) Using deep learning to identify diabetic retinopathy in Saudi Arabia. Comput Methods Programs Biomed 205:105992. https:\/\/doi.org\/10.1016\/j.cmpb.2021.105992","journal-title":"Comput Methods Programs Biomed"},{"key":"2688_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksuci.2021.06.006","author":"D Nagpal","year":"2021","unstructured":"Nagpal D, Panda SN, Malarvel M, Pattanaik PA, Zubair Khan M (2021) A review of diabetic retinopathy: datasets, approaches, evaluation metrics and future trends. J King Saud Univ Comput Inf Sci. https:\/\/doi.org\/10.1016\/j.jksuci.2021.06.006","journal-title":"J King Saud Univ Comput Inf Sci"},{"key":"2688_CR5","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.neunet.2019.01.015","volume":"113","author":"M Kampffmeyer","year":"2019","unstructured":"Kampffmeyer M, L\u00f8kse S, Bianchi FM, Livi L, Salberg AB, Jenssen R (2019) Deep divergence-based approach to clustering. Neural Netw 113:91\u2013101. https:\/\/doi.org\/10.1016\/j.neunet.2019.01.015","journal-title":"Neural Netw"},{"key":"2688_CR6","doi-asserted-by":"publisher","unstructured":"Raheja S, Kasturia S, Cheng X, Kumar M (2021) Machine learning-based diffusion model for prediction of coronavirus-19 outbreak. Neural Comput Appl 0123456789. https:\/\/doi.org\/10.1007\/s00521-021-06376-x","DOI":"10.1007\/s00521-021-06376-x"},{"key":"2688_CR7","doi-asserted-by":"publisher","unstructured":"Chakradar M, Aggarwal A, Cheng X, Rani A, Kumar M, Shankar A (2021) A non-invasive approach to identify insulin resistance with triglycerides and HDL-c ratio using machine learning. Neural Process Lett 0123456789. https:\/\/doi.org\/10.1007\/s11063-021-10461-6","DOI":"10.1007\/s11063-021-10461-6"},{"key":"2688_CR8","doi-asserted-by":"publisher","unstructured":"Thapliyal M, Ahuja NJ, Shankar A, Cheng X, Kumar M (2021) A differentiated learning environment in domain model for learning disabled learners. J Comput High Educ 0123456789. https:\/\/doi.org\/10.1007\/s12528-021-09278-y","DOI":"10.1007\/s12528-021-09278-y"},{"key":"2688_CR9","doi-asserted-by":"publisher","first-page":"11093","DOI":"10.1109\/ACCESS.2019.2891970","volume":"7","author":"J Enguehard","year":"2019","unstructured":"Enguehard J, O\u2019Halloran P, Gholipour A (2019) Semi-supervised learning with deep embedded clustering for image classification and segmentation. IEEE Access 7:11093\u201311104. https:\/\/doi.org\/10.1109\/ACCESS.2019.2891970","journal-title":"IEEE Access"},{"key":"2688_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2021.106261","volume":"208","author":"M van Eijnatten","year":"2021","unstructured":"van Eijnatten M, Leonardo Rundo K, Batenburg J, Lucka F, Beddowes E, Caldas C, Gallagher FA, Sala E, Sch\u00f6nlieb CB, Woitek R (2021) 3D deformable registration of longitudinal abdominopelvic CT images using unsupervised deep learning. Comput Methods Programs Biomed 208:106261. https:\/\/doi.org\/10.1016\/j.cmpb.2021.106261","journal-title":"Comput Methods Programs Biomed"},{"key":"2688_CR11","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1016\/j.neucom.2014.12.123","volume":"173","author":"Q Zou","year":"2016","unstructured":"Zou Q, Zeng J, Cao L, Ji R (2016) A novel features ranking metric with application to scalable visual and bioinformatics data classification. Neurocomputing 173:346\u2013354. https:\/\/doi.org\/10.1016\/j.neucom.2014.12.123","journal-title":"Neurocomputing"},{"issue":"2","key":"2688_CR12","doi-asserted-by":"publisher","first-page":"140","DOI":"10.7763\/ijeeee.2012.v2.97","volume":"2","author":"S Agarwal","year":"2012","unstructured":"Agarwal S (2012) Data mining in education: data classification and decision tree approach. Int J E-Educ e-Bus e-Manag e-Learn 2(2):140\u2013144. https:\/\/doi.org\/10.7763\/ijeeee.2012.v2.97","journal-title":"Int J E-Educ e-Bus e-Manag e-Learn"},{"key":"2688_CR13","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1007\/978-981-10-5427-3_15","volume":"721","author":"A Rani","year":"2017","unstructured":"Rani A, Kumar M, Goel P (2017) Image modelling: a feature detection approach for steganalysis. Commun Comput Inf Sci 721:140\u2013148. https:\/\/doi.org\/10.1007\/978-981-10-5427-3_15","journal-title":"Commun Comput Inf Sci"},{"issue":"4","key":"2688_CR14","doi-asserted-by":"publisher","first-page":"60","DOI":"10.4018\/IJEHMC.20210701.oa4","volume":"12","author":"SK Punia","year":"2021","unstructured":"Punia SK, Kumar M, Stephan T, Deverajan GG, Patan R (2021) Performance analysis of machine learning algorithms for big data classification: Ml and Ai-based algorithms for big data analysis. Int J E-Health Med Commun 12(4):60\u201375. https:\/\/doi.org\/10.4018\/IJEHMC.20210701.oa4","journal-title":"Int J E-Health Med Commun"},{"issue":"6","key":"2688_CR15","doi-asserted-by":"publisher","first-page":"1720","DOI":"10.1109\/TBME.2012.2193126","volume":"59","author":"B Antal","year":"2012","unstructured":"Antal B, Hajdu A (2012) An ensemble-based system for microaneurysm detection and diabetic retinopathy grading. IEEE Trans Biomed Eng 59(6):1720\u20131726. https:\/\/doi.org\/10.1109\/TBME.2012.2193126","journal-title":"IEEE Trans Biomed Eng"},{"issue":"9","key":"2688_CR16","doi-asserted-by":"publisher","first-page":"1223","DOI":"10.1109\/TMI.2006.879953","volume":"25","author":"AD Fleming","year":"2006","unstructured":"Fleming AD, Philip S, Goatman KA, Olson JA, Sharp PF (2006) Automated microaneurysm detection using local contrast normalization and local vessel detection. IEEE Trans Med Imaging 25(9):1223\u20131232. https:\/\/doi.org\/10.1109\/TMI.2006.879953","journal-title":"IEEE Trans Med Imaging"},{"issue":"1","key":"2688_CR17","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1109\/TMI.2009.2033909","volume":"29","author":"M Niemeijer","year":"2010","unstructured":"Niemeijer M, Van Ginneken B, Cree MJ, Mizutani A, Quellec G, Sanchez CI, Zhang B et al (2010) Retinopathy online challenge: automatic detection of microaneurysms in digital color fundus photographs. IEEE Trans Med Imaging 29(1):185\u2013195. https:\/\/doi.org\/10.1109\/TMI.2009.2033909","journal-title":"IEEE Trans Med Imaging"},{"key":"2688_CR18","doi-asserted-by":"publisher","unstructured":"Zhou W, Chengdong Wu, Chen D, Wang Z, Yi Y, Wenyou Du (2017) Automatic microaneurysms detection based on multifeature fusion dictionary learning. Comput Math Methods Med 2017. https:\/\/doi.org\/10.1155\/2017\/2483137","DOI":"10.1155\/2017\/2483137"},{"issue":"2","key":"2688_CR19","doi-asserted-by":"publisher","first-page":"292","DOI":"10.1007\/s40846-020-00509-8","volume":"40","author":"N Mazlan","year":"2020","unstructured":"Mazlan N, Yazid H, Arof H, Isa HM (2020) Automated microaneurysms detection and classification using multilevel thresholding and multilayer perceptron. J Med Biol Eng 40(2):292\u2013306. https:\/\/doi.org\/10.1007\/s40846-020-00509-8","journal-title":"J Med Biol Eng"},{"key":"2688_CR20","doi-asserted-by":"publisher","first-page":"11946","DOI":"10.1109\/ACCESS.2018.2890426","volume":"7","author":"K Wisaeng","year":"2019","unstructured":"Wisaeng K, Sa-Ngiamvibool W (2019) Exudates detection using morphology mean shift algorithm in retinal images. IEEE Access 7:11946\u201311958. https:\/\/doi.org\/10.1109\/ACCESS.2018.2890426","journal-title":"IEEE Access"},{"key":"2688_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2019\/4248218","volume":"2019","author":"D Pei","year":"2019","unstructured":"Pei D, Zhang C, Quan Y, Guo Q (2019) Identification of potential type II diabetes in a Chinese population with a sensitive decision tree approach. J Diabetes Res 2019:1\u20137. https:\/\/doi.org\/10.1155\/2019\/4248218","journal-title":"J Diabetes Res"},{"key":"2688_CR22","doi-asserted-by":"publisher","unstructured":"Joshi S, Karule PT (2018) Detection of hard exudates based on morphological feature extraction. Biomed Pharmacol J 11(1): 215\u201325. https:\/\/doi.org\/10.13005\/bpj\/1366.","DOI":"10.13005\/bpj\/1366"},{"key":"2688_CR23","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1007\/978-3-319-21963-9_33","volume":"9218","author":"Z Xiao","year":"2015","unstructured":"Xiao Z, Li F, Geng L, Zhang F, Jun Wu, Zhang X, Long Su et al (2015) Hard exudates detection method based on background-estimation. Lecture Notes Comput Sci 9218:361\u2013372. https:\/\/doi.org\/10.1007\/978-3-319-21963-9_33","journal-title":"Lecture Notes Comput Sci"},{"key":"2688_CR24","doi-asserted-by":"publisher","unstructured":"Sreng S, Maneerat N, Isarakorn D, Hamamoto K, Panjaphongse R (2015) Automatic hemorrhages detection based on fundus images. Proceedings - 2015 7th International Conference on Information Technology and Electrical Engineering: Envisioning the Trend of Computer, Information and Engineering, ICITEE 2015, 253\u201357. https:\/\/doi.org\/10.1109\/ICITEED.2015.7408951","DOI":"10.1109\/ICITEED.2015.7408951"},{"key":"2688_CR25","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1109\/isbi.2014.6867819","volume":"2014","author":"L S\u00e9oud","year":"2014","unstructured":"S\u00e9oud L, Faucon T, Hurtut T, Chelbi J, Cheriet F, Pierre Langlois JM (2014) Automatic detection of microaneurysms and haemorrhages in fundus images using dynamic shape features. 2014 IEEE 11th International Symposium on Biomedical Imaging ISBI 2014:101\u2013104. https:\/\/doi.org\/10.1109\/isbi.2014.6867819","journal-title":"2014 IEEE 11th International Symposium on Biomedical Imaging ISBI"},{"key":"2688_CR26","doi-asserted-by":"publisher","unstructured":"Makarov S, Horner M, Noetscher G, Modeling CH (2021) Brain and Human Body Modeling 2020. Brain Hum Body Model 2020. https:\/\/doi.org\/10.1007\/978-3-030-45623-8","DOI":"10.1007\/978-3-030-45623-8"},{"issue":"6","key":"2688_CR27","doi-asserted-by":"publisher","first-page":"8007","DOI":"10.1007\/s11042-021-11824-w","volume":"81","author":"S Das","year":"2022","unstructured":"Das S, Saha SK (2022) Diabetic retinopathy detection and classification using CNN tuned by genetic algorithm. Multimed Tools Appl 81(6):8007\u20138020. https:\/\/doi.org\/10.1007\/s11042-021-11824-w","journal-title":"Multimed Tools Appl"},{"key":"2688_CR28","doi-asserted-by":"publisher","unstructured":"Gupta S, Thakur S, Gupta A (2022) Optimized hybrid machine learning approach for smartphone-based diabetic retinopathy detection. Multimed Tools Appl 1\u201327. https:\/\/doi.org\/10.1007\/s11042-022-12103-y","DOI":"10.1007\/s11042-022-12103-y"},{"issue":"9","key":"2688_CR29","doi-asserted-by":"publisher","first-page":"1059","DOI":"10.2174\/1573405617666210127154257","volume":"17","author":"K Raza","year":"2021","unstructured":"Raza K, Singh NK (2021) A tour of unsupervised deep learning for medical image analysis. Curr Med Imaging 17(9):1059\u20131077. https:\/\/doi.org\/10.2174\/1573405617666210127154257","journal-title":"Curr Med Imaging"},{"key":"2688_CR30","doi-asserted-by":"publisher","first-page":"150530","DOI":"10.1109\/ACCESS.2019.2947484","volume":"7","author":"S Qummar","year":"2019","unstructured":"Qummar S, Khan FG, Shah S, Khan A, Shamshirband S, Rehman ZU, Jadoon W (2019) A deep learning ensemble approach for diabetic retinopathy detection. IEEE Access 7:150530\u2013150539. https:\/\/doi.org\/10.1109\/ACCESS.2019.2947484","journal-title":"IEEE Access"},{"issue":"6","key":"2688_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/electronics9060914","volume":"9","author":"JD Bodapati","year":"2020","unstructured":"Bodapati JD, Veeranjaneyulu N, Shareef SN, Hakak S, Bilal M, Maddikunta PKR, Jo O (2020) Blended multi-modal deep convnet features for diabetic retinopathy severity prediction. Electronics (Switzerland) 9(6):1\u201316. https:\/\/doi.org\/10.3390\/electronics9060914","journal-title":"Electronics (Switzerland)"},{"key":"2688_CR32","doi-asserted-by":"publisher","unstructured":"Sikder N (2019) Early blindness detection based on retinal images using ensemble learning. . https:\/\/doi.org\/10.1109\/ICCIT48885.2019.9038439","DOI":"10.1109\/ICCIT48885.2019.9038439"},{"key":"2688_CR33","doi-asserted-by":"publisher","unstructured":"Kauppi T, Kalesnykiene V, Kamarainen JK, Lensu L, Sorri I, Raninen A, Voutilainen R, Pietil\u00e4 J, K\u00e4lvi\u00e4inen H, Uusitalo H (2007) The DIARETDB1 diabetic retinopathy database and evaluation protocol. BMVC 2007 - Proceedings of the British Machine Vision Conference 2007, 1\u201318. https:\/\/doi.org\/10.5244\/C.21.15","DOI":"10.5244\/C.21.15"},{"key":"2688_CR34","doi-asserted-by":"publisher","unstructured":"Somasundaram SK, Alli P (2017) A machine learning ensemble classifier for early prediction of diabetic retinopathy. J Med Syst 41(12). https:\/\/doi.org\/10.1007\/s10916-017-0853-x","DOI":"10.1007\/s10916-017-0853-x"},{"key":"2688_CR35","doi-asserted-by":"publisher","unstructured":"Xu K, Feng D, Mi H (2017) Deep convolutional neural network-based early automated detection of diabetic retinopathy using fundus image. Molecules 22(12). https:\/\/doi.org\/10.3390\/molecules22122054","DOI":"10.3390\/molecules22122054"},{"issue":"2","key":"2688_CR36","doi-asserted-by":"publisher","first-page":"1503","DOI":"10.1002\/jcb.27279","volume":"120","author":"Y Wang","year":"2019","unstructured":"Wang Y, Wang Z, Zhang H (2019) Identification of diagnostic biomarker in patients with gestational diabetes mellitus based on transcriptome-wide gene expression and pattern recognition. J Cell Biochem 120(2):1503\u20131510. https:\/\/doi.org\/10.1002\/jcb.27279","journal-title":"J Cell Biochem"},{"key":"2688_CR37","doi-asserted-by":"crossref","unstructured":"Shrestha H, Dhasarathan C, Kumar M, Nidhya R, Shankar A, Kumar M (2020) A Deep learning based convolution neural network-DCNN approach to detect brain tumor. Proceedings of Academia-Industry Consortium for Data Science: AICDS, p 115","DOI":"10.1007\/978-981-16-6887-6_11"},{"key":"2688_CR38","doi-asserted-by":"publisher","unstructured":"Pham QTM, Ahn S, Shin J, Song SJ (2022) Generating future fundus images for early age-related macular degeneration based on generative adversarial networks. Comput Methods Programs Biomed 216:106648. https:\/\/doi.org\/10.1016\/j.cmpb.2022.106648","DOI":"10.1016\/j.cmpb.2022.106648"},{"issue":"6","key":"2688_CR39","first-page":"438","volume":"11","author":"TKW Tam","year":"2005","unstructured":"Tam TKW, Lau CM, Tsang LCY, Ng KK, Ho KS, Lai TC (2005) Epidemiological study of diabetic retinopathy in a primary care setting in Hong Kong. Hong Kong Med J 11(6):438\u2013444","journal-title":"Hong Kong Med J"},{"key":"2688_CR40","doi-asserted-by":"publisher","first-page":"543","DOI":"10.1016\/j.asoc.2015.12.022","volume":"46","author":"H Verma","year":"2016","unstructured":"Verma H, Agrawal RK, Sharan A (2016) An improved intuitionistic fuzzy c-means clustering algorithm incorporating local information for brain image segmentation. Appl Soft Comput J 46:543\u2013557. https:\/\/doi.org\/10.1016\/j.asoc.2015.12.022","journal-title":"Appl Soft Comput J"},{"issue":"17","key":"2688_CR41","doi-asserted-by":"publisher","first-page":"6997","DOI":"10.1016\/j.eswa.2013.06.022","volume":"40","author":"C Birtolo","year":"2013","unstructured":"Birtolo C, Ronca D (2013) Advances in clustering collaborative filtering by means of fuzzy c-means and trust. Expert Syst Appl 40(17):6997\u20137009. https:\/\/doi.org\/10.1016\/j.eswa.2013.06.022","journal-title":"Expert Syst Appl"},{"key":"2688_CR42","unstructured":"Bartunov S, Santoro A, Richards B, Marris L, Hinton GE, Lillicrap T (2018) Assessing the scalability of biologically-motivated deep learning algorithms and architectures. In: Advances in Neural Information Processing Systems, pp 9368\u20139378"},{"key":"2688_CR43","doi-asserted-by":"publisher","unstructured":"Nayak J, Naik B, Behera HS (2015) Fuzzy C-means (FCM) clustering algorithm: a decade review from 2000 to 2014\u2019, Computational intelligence in data mining. Springer, New Delhi, vol 2, pp 133-149.https:\/\/doi.org\/10.1007\/978-81-322-2208-8_14","DOI":"10.1007\/978-81-322-2208-8_14"},{"key":"2688_CR44","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1007\/978-81-322-2208-8_14","volume":"233","author":"IB Aydilek","year":"2013","unstructured":"Aydilek IB, Arslan A (2013) A hybrid method for imputation of missing values using optimized fuzzy c-means with support vector regression and a genetic algorithm. Inf Sci 233:25\u201335. https:\/\/doi.org\/10.1007\/978-81-322-2208-8_14","journal-title":"Inf Sci"},{"issue":"10","key":"2688_CR45","doi-asserted-by":"publisher","first-page":"1701","DOI":"10.3390\/s16101701","volume":"16","author":"T Ma","year":"2016","unstructured":"Ma T, Wang F, Cheng J, Yu Y, Chen X (2016) A hybrid spectral clustering and deep neural network ensemble algorithm for intrusion detection in sensor networks. Sensors 16(10):1701. https:\/\/doi.org\/10.3390\/s16101701","journal-title":"Sensors"},{"key":"2688_CR46","doi-asserted-by":"publisher","unstructured":"Deng Y, Ren Z, Kong Y, Bao F, Dai Q (2016) A hierarchical fused fuzzy deep neural network for data classification. IEEE Trans Fuzzy Syst 25(4):1006\u20131012. https:\/\/doi.org\/10.1145\/3139337.3139345","DOI":"10.1145\/3139337.3139345"},{"issue":"2","key":"2688_CR47","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1109\/TIP.2012.2219547","volume":"22","author":"M Gong","year":"2012","unstructured":"Gong M, Liang Y, Shi J, Ma W, Ma J (2012) Fuzzy c-means clustering with local information and kernel metric for image segmentation. IEEE Trans Image Process 22(2):573\u2013584. https:\/\/doi.org\/10.1109\/TIP.2012.2219547","journal-title":"IEEE Trans Image Process"},{"issue":"22","key":"2688_CR48","doi-asserted-by":"publisher","first-page":"8520","DOI":"10.1016\/j.eswa.2015.07.007","volume":"42","author":"M Bennasar","year":"2015","unstructured":"Bennasar M, Hicks Y, Setchi R (2015) Feature selection using joint mutual information maximisation. Expert Syst Appl 42(22):8520\u20138532. https:\/\/doi.org\/10.1016\/j.eswa.2015.07.007","journal-title":"Expert Syst Appl"},{"key":"2688_CR49","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1016\/j.socnet.2015.03.002","volume":"42","author":"M Hoffman","year":"2015","unstructured":"Hoffman M, Steinley D, Brusco MJ (2015) A note on using the adjusted Rand index for link prediction in networks. Soc Netw 42:72\u201379. https:\/\/doi.org\/10.1016\/j.socnet.2015.03.002","journal-title":"Soc Netw"},{"key":"2688_CR50","doi-asserted-by":"publisher","unstructured":"Jaafar HF, Nandi AK, Al-Nuaimy W (2011) Automated detection of red lesions from digital colour fundus photographs. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, p 6232\u201335.https:\/\/doi.org\/10.1109\/IEMBS.2011.6091539","DOI":"10.1109\/IEMBS.2011.6091539"},{"issue":"5","key":"2688_CR51","doi-asserted-by":"publisher","first-page":"775","DOI":"10.1109\/TMI.2008.2012029","volume":"28","author":"M Niemeijer","year":"2009","unstructured":"Niemeijer M, Abr\u00e0moff MD, Van Ginneken B (2009) Information fusion for diabetic retinopathy CAD in digital color fundus photographs. IEEE Trans Med Imaging 28(5):775\u2013785. https:\/\/doi.org\/10.1109\/TMI.2008.2012029","journal-title":"IEEE Trans Med Imaging"},{"key":"2688_CR52","doi-asserted-by":"publisher","unstructured":"Ravishankar S, Jain A, Mittal A (2009) Automated feature extraction for early detection of diabetic retinopathy in fundus images. 2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, 210\u201317. https:\/\/doi.org\/10.1109\/CVPRW.2009.5206763","DOI":"10.1109\/CVPRW.2009.5206763"},{"issue":"4","key":"2688_CR53","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1007\/s10278-009-9246-0","volume":"23","author":"GB Kande","year":"2010","unstructured":"Kande GB, Satya Savithri T, Venkata Subbaiah P (2010) Automatic detection of microaneurysms and hemorrhages in digital fundus images. J Digit Imaging 23(4):430\u2013437. https:\/\/doi.org\/10.1007\/s10278-009-9246-0","journal-title":"J Digit Imaging"},{"key":"2688_CR54","doi-asserted-by":"publisher","unstructured":"Shahin EM, Taha TE, Al-Nuaimy W, El Rabaie S, Zahran OF, El-Samie FEA (2012) \u201cAutomated detection of diabetic retinopathy in blurred digital fundus images. 2012 8th International Computer Engineering Conference: Today Information Society ICENCO 2012, 20\u201325. https:\/\/doi.org\/10.1109\/ICENCO.2012.6487084","DOI":"10.1109\/ICENCO.2012.6487084"},{"key":"2688_CR55","doi-asserted-by":"publisher","unstructured":"Roychowdhury S, Koozekanani DD, Parhi KK (2012) Screening fundus images for diabetic retinopathy. Conf Rec Asilomar Conf Signals Syst Comput 1641\u201345. https:\/\/doi.org\/10.1109\/ACSSC.2012.6489310","DOI":"10.1109\/ACSSC.2012.6489310"},{"key":"2688_CR56","doi-asserted-by":"publisher","unstructured":"Mishra S, Hanchate S, Saquib Z (2020) Diabetic retinopathy detection using deep learning. Proceedings of the International Conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2020, 515\u201320. https:\/\/doi.org\/10.1109\/ICSTCEE49637.2020.9277506","DOI":"10.1109\/ICSTCEE49637.2020.9277506"}],"container-title":["Medical & Biological Engineering & Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-022-02688-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-022-02688-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-022-02688-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,9]],"date-time":"2022-11-09T17:16:32Z","timestamp":1668014192000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-022-02688-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,24]]},"references-count":56,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["2688"],"URL":"https:\/\/doi.org\/10.1007\/s11517-022-02688-9","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,24]]},"assertion":[{"value":"22 March 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 October 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 October 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":"There is no direct human participation in the manuscript.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Research involving human participants and animals"}},{"value":"Informed consent was obtained from all individual participants involved in the study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}