{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T18:27:37Z","timestamp":1775845657041,"version":"3.50.1"},"reference-count":146,"publisher":"Springer Science and Business Media LLC","issue":"18","license":[{"start":{"date-parts":[[2022,3,23]],"date-time":"2022-03-23T00:00:00Z","timestamp":1647993600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,3,23]],"date-time":"2022-03-23T00:00:00Z","timestamp":1647993600000},"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"],"published-print":{"date-parts":[[2022,7]]},"DOI":"10.1007\/s11042-022-12642-4","type":"journal-article","created":{"date-parts":[[2022,3,23]],"date-time":"2022-03-23T04:44:04Z","timestamp":1648010644000},"page":"25613-25655","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":113,"title":["A critical review on diagnosis of diabetic retinopathy using machine learning and deep learning"],"prefix":"10.1007","volume":"81","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1518-3332","authenticated-orcid":false,"given":"Dolly","family":"Das","sequence":"first","affiliation":[]},{"given":"Saroj Kr.","family":"Biswas","sequence":"additional","affiliation":[]},{"given":"Sivaji","family":"Bandyopadhyay","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,3,23]]},"reference":[{"key":"12642_CR1","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1016\/j.asoc.2016.08.015","volume":"49","author":"S Abed","year":"2016","unstructured":"Abed S, Al-Roomi SA, Al-Shayeji M (2016) Effective optic disc detection method based on swarm intelligence techniques and novel pre-processing steps. Appl Soft Comput 49:146\u2013163. https:\/\/doi.org\/10.1016\/j.asoc.2016.08.015","journal-title":"Appl Soft Comput"},{"issue":"13","key":"12642_CR2","doi-asserted-by":"publisher","first-page":"5200","DOI":"10.1167\/iovs.16-19964","volume":"57","author":"MD Abr\u00e0moff","year":"2016","unstructured":"Abr\u00e0moff MD, Lou Y, Erginay A, Clarida W, Amelon R, Folk JC, Niemeijer M (2016) Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Invest Ophthalmol Vis Sci 57(13):5200\u20135206. https:\/\/doi.org\/10.1167\/iovs.16-19964","journal-title":"Invest Ophthalmol Vis Sci"},{"issue":"4","key":"12642_CR3","doi-asserted-by":"publisher","first-page":"1328","DOI":"10.1109\/JBHI.2013.2296399","volume":"18","author":"C Agurto","year":"2014","unstructured":"Agurto C, Murray V, Yu H, Wigdahl J, Pattichis M, Nemeth S, Barriga ES, Soliz P (2014) A multiscale optimization approach to detect exudates in the macula. IEEE J Biomed Health Inf 18(4):1328\u20131336. https:\/\/doi.org\/10.1109\/JBHI.2013.2296399","journal-title":"IEEE J Biomed Health Inf"},{"issue":"1","key":"12642_CR4","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.patcog.2012.07.002","volume":"46","author":"MU Akram","year":"2013","unstructured":"Akram MU, Khalid S, Khan SA (2013) Identification and classification of microaneurysms for early detection of diabetic retinopathy. Pattern Recogn 46(1):107\u2013116. https:\/\/doi.org\/10.1016\/j.patcog.2012.07.002","journal-title":"Pattern Recogn"},{"key":"12642_CR5","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1016\/j.compbiomed.2013.11.014","volume":"45","author":"MU Akram","year":"2014","unstructured":"Akram MU, Khalid S, Tariq A, Khan SA, Azam F (2014) Detection and classification of retinal lesions for grading of diabetic retinopathy. Comput Biol Med 45:161\u2013171. https:\/\/doi.org\/10.1016\/j.compbiomed.2013.11.014","journal-title":"Comput Biol Med"},{"key":"12642_CR6","volume-title":"Automated detection of diabetic retinopathy using fluorescein angiography photographs","author":"M Alban","year":"2016","unstructured":"Alban M, Gilligan T (2016) Automated detection of diabetic retinopathy using fluorescein angiography photographs. Report of Standford Education."},{"key":"12642_CR7","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.bspc.2017.09.008","volume":"40","author":"B Al-Bander","year":"2017","unstructured":"Al-Bander B, Al-Nuaimy W, Williams BM, Zheng Y (2017) Multiscale sequential convolutional neural networks for simultaneous detection of fovea and optic disc. Biomed Signal Process Control 40:91\u2013101. https:\/\/doi.org\/10.1016\/j.bspc.2017.09.008","journal-title":"Biomed Signal Process Control"},{"key":"12642_CR8","doi-asserted-by":"publisher","unstructured":"Alghamdi HS, Tang HL, Waheeb SA, Peto T (2016) Automatic optic disc abnormality detection in fundus images: a DL approach. Proceedings of the ophthalmic medical image analysis international workshop. Pp. 17-24. https:\/\/doi.org\/10.17077\/omia.1042","DOI":"10.17077\/omia.1042"},{"key":"12642_CR9","doi-asserted-by":"publisher","unstructured":"Alom MZ, Taha TM, Yakopcic C, Westberg S, Sidike P, Nasrin MS, Hasan M, Van Essen BC, Awwal AAS, Asari VK (2019) A State-of-the-Art Survey on Deep Learning Theory and Architectures, electronics, 8(3):1\u201366. https:\/\/doi.org\/10.3390\/electronics8030292","DOI":"10.3390\/electronics8030292"},{"key":"12642_CR10","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1016\/j.jocs.2017.01.002","volume":"19","author":"J Amin","year":"2017","unstructured":"Amin J, Sharif M, Yasmin M, Ali H, Fernandes SL (2017) A method for the detection and classification of diabetic retinopathy using structural predictors of bright lesions. J Comput Sci 19:153\u2013164. https:\/\/doi.org\/10.1016\/j.jocs.2017.01.002","journal-title":"J Comput Sci"},{"key":"12642_CR11","unstructured":"Andrew D, MD, PhD, University of Iowa, Retina, Diabetic Retinopathy, EyeRounds.org Available at - https:\/\/webeye.ophth.uiowa.edu\/eyeforum\/atlas\/photos\/DR\/DR.jpg, Accessed on \u2013 13-10-2020"},{"issue":"4","key":"12642_CR12","doi-asserted-by":"publisher","first-page":"1129","DOI":"10.1109\/JBHI.2015.2440091","volume":"20","author":"R Annunziata","year":"2015","unstructured":"Annunziata R, Garzelli A, Ballerini L, Mecocci A, Trucco E (2015) Leveraging multiscale hessian-based enhancement with a novel exudate Inpainting technique for retinal vessel segmentation. IEEE J Biomed Health Inf 20(4):1129\u20131138.https:\/\/doi.org\/10.1109\/JBHI.2015.2440091","journal-title":"IEEE J Biomed Health Inf"},{"issue":"6","key":"12642_CR13","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"},{"key":"12642_CR14","doi-asserted-by":"publisher","unstructured":"Antal B, L\u00e1z\u00e1r I, Hajdu A (2012) An adaptive weighting approach for ensemble-based detection of microaneurysms in color fundus images. 2012 annual international conference of the IEEE engineering in medicine and biology society. Pp. 5955-5958. https:\/\/doi.org\/10.1109\/EMBC.2012.6347350","DOI":"10.1109\/EMBC.2012.6347350"},{"issue":"1","key":"12642_CR15","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.compbiomed.2014.10.007","volume":"55","author":"A Aquino","year":"2014","unstructured":"Aquino A (2014) Establishing the macular grading grid by means of fovea Centre detection using anatomical-based and visual-based features. Comput Biol Med 55(1):61\u201373. https:\/\/doi.org\/10.1016\/j.compbiomed.2014.10.007","journal-title":"Comput Biol Med"},{"key":"12642_CR16","doi-asserted-by":"publisher","unstructured":"Argade KS, Deshmukh KA, Narkhede MM, Sonawane NN, Jore S (2015) Automatic detection of diabetic retinopathy using image processing and data mining techniques. Proceedings of the 2015 international conference on Green computing and internet of things (ICGCIoT\u201915). 517-521. https:\/\/doi.org\/10.1109\/ICGCIoT.2015.7380519","DOI":"10.1109\/ICGCIoT.2015.7380519"},{"key":"12642_CR17","doi-asserted-by":"publisher","unstructured":"Asha PR, Karpagavalli S (2015) Diabetic retinal exudates detection using extreme learning machine. Emerging ICT for bridging the future-proceedings of the 49th annual convention of the Computer Society of India CSI. 2:573-578. https:\/\/doi.org\/10.1109\/ICACCS.2015.7324057","DOI":"10.1109\/ICACCS.2015.7324057"},{"issue":"1","key":"12642_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1756-0500-2-196","volume":"2","author":"T Aslam","year":"2009","unstructured":"Aslam T, Chua P, Richardson M, Patel P, Musadiq M (2009) A system for computerised retinal haemorrhage analysis. BMC Res Notes 2(1):1\u20136. https:\/\/doi.org\/10.1186\/1756-0500-2-196","journal-title":"BMC Res Notes"},{"issue":"4","key":"12642_CR19","doi-asserted-by":"publisher","first-page":"679","DOI":"10.1016\/j.bbe.2016.07.001","volume":"36","author":"S Banerjee","year":"2016","unstructured":"Banerjee S, Kayal D (2016) Detection of hard exudates using mean shift and normalized cut method. Biocybern Biomed Eng 36(4):679\u2013685. https:\/\/doi.org\/10.1016\/j.bbe.2016.07.001","journal-title":"Biocybern Biomed Eng"},{"issue":"1","key":"12642_CR20","doi-asserted-by":"publisher","first-page":"92","DOI":"10.11591\/eei.v5i1.553","volume":"5","author":"VR Bhargavi","year":"2016","unstructured":"Bhargavi VR, Senapati RK (2016) Bright lesion detection in color fundus images based on texture features. Bull Electric Eng Inf 5(1):92\u2013100. https:\/\/doi.org\/10.11591\/eei.v5i1.553","journal-title":"Bull Electric Eng Inf"},{"key":"12642_CR21","doi-asserted-by":"publisher","first-page":"923","DOI":"10.1007\/s11760-020-01816-y","volume":"15","author":"JD Bodapati","year":"2021","unstructured":"Bodapati JD, Shaik NS, Naralasetti V (2021) Deep convolution feature aggregation: an application to diabetic retinopathy severity level prediction. SIViP 15:923\u2013930. https:\/\/doi.org\/10.1007\/s11760-020-01816-y","journal-title":"SIViP"},{"key":"12642_CR22","doi-asserted-by":"publisher","unstructured":"Buades A, Coll B, Morel JM (2005) A non-local algorithm for image denoising. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2: 60\u201365. https:\/\/doi.org\/10.1109\/CVPR.2005.38","DOI":"10.1109\/CVPR.2005.38"},{"key":"12642_CR23","unstructured":"Chaturvedi SS, Gupta K, Ninawe V, Prasad PS (2020) Automated diabetic retinopathy grading using deep convolutional neural network. arXiv:2004.06334:1-12"},{"key":"12642_CR24","doi-asserted-by":"publisher","first-page":"4954","DOI":"10.1109\/EMBC.2012.6347104","volume":"2012","author":"X Cheng","year":"2012","unstructured":"Cheng X, Wong DWK, Liu J, Lee BH, Tan NM, Zhang J, Cheng CY, Cheung G, Wong TY (2012) Automatic localization of retinal landmarks. Ann Int Conf IEEE Eng Med Biol Soc 2012:4954\u20134957. https:\/\/doi.org\/10.1109\/EMBC.2012.6347104","journal-title":"Ann Int Conf IEEE Eng Med Biol Soc"},{"key":"12642_CR25","doi-asserted-by":"publisher","unstructured":"Chetoui M, Akhloufi MA (2020) Explainable diabetic retinopathy using EfficientNET. In 2020 42nd annual international conference of the IEEE engineering in Medicine & Biology Society (EMBC).1966-1969. https:\/\/doi.org\/10.1109\/EMBC44109.2020.9175664","DOI":"10.1109\/EMBC44109.2020.9175664"},{"key":"12642_CR26","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/j.cmpb.2018.02.016","volume":"158","author":"P Chudzik","year":"2018","unstructured":"Chudzik P, Majumdar S, Caliv\u00e1a F, Al-Diri B, Hunter A (2018) Microaneurysm detection using fully convolutional neural networks. Comput Methods Prog Biomed 158:185\u2013192. https:\/\/doi.org\/10.1016\/j.cmpb.2018.02.016","journal-title":"Comput Methods Prog Biomed"},{"issue":"5","key":"12642_CR27","doi-asserted-by":"publisher","first-page":"1149","DOI":"10.1109\/TMI.2018.2794988","volume":"37","author":"L Dai","year":"2018","unstructured":"Dai L, Fang R, Li H, Hou X, Sheng B, Wu Q, Jia W (2018) Clinical report guided retinal microaneurysm detection with multi-sieving deep learning. IEEE Trans Med Imaging 37(5):1149\u20131161. https:\/\/doi.org\/10.1109\/TMI.2018.2794988","journal-title":"IEEE Trans Med Imaging"},{"issue":"2","key":"12642_CR28","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1016\/j.irbm.2013.01.010","volume":"34","author":"E Decenci\u00e8re","year":"2013","unstructured":"Decenci\u00e8re E, Cazuguel G, Zhang X, Thibault G, Klein JC, Meyer F, Marcotegui B, Quellec G, Lamard M, Danno R, Elie D, Massin P, Viktor Z, Erginay A, Lay B, Chabouis A (2013) TeleOphta: machine learning and image processing methods for teleophthalmology. IRBM. 34(2):196\u2013203. https:\/\/doi.org\/10.1016\/j.irbm.2013.01.010","journal-title":"IRBM."},{"key":"12642_CR29","unstructured":"Diabetic Retinopathy \u2013 Features of Diabetes: Cotton Wool Spots, Glycosmedia, Diabetes News Service, Available at https:\/\/www.glycosmedia.com\/education\/diabetic-retinopathy\/diabetic-retinopathy-features-of-diabetes-cotton-wool-spots, Accessed on 12-06-2020"},{"key":"12642_CR30","unstructured":"Diabetic Retinopathy \u2013 Features of Diabetes : Intraretinal Haemorrhages, Glycosmedia, Diabetes News Service, Available at https:\/\/www.glycosmedia.com\/education\/diabetic-retinopathy\/diabetic-retinopathy-features-of-diabetes-intraretinal-haemorrhages, Accessed on 12-06-2020"},{"key":"12642_CR31","unstructured":"Diabetic retinopathy, American Optometric Association, https:\/\/www.aoa.org\/patients-and-public\/eye-and-vision-problems\/glossary-of-eye-and-vision-conditions\/diabetic-retinopathy-Over-time-diabetes-damages-small-condition-usually-affects-both-eyes, Accessed on 13-05-2020."},{"key":"12642_CR32","unstructured":"Diabetic Retinopathy Detection, Kaggle repository, Available at: https:\/\/www.kaggle.com\/c\/diabetic-retinopathy-detection\/data, Accessed on 14-06-2021"},{"issue":"1","key":"12642_CR33","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.compmedimag.2010.09.004","volume":"35","author":"C Duanggate","year":"2011","unstructured":"Duanggate C, Uyyanonvara B, Makhanov SS, Barman S, Williamson T (2011) Parameter-free optic disc detection. Comput Med Imaging Graph 35(1):51\u201363. https:\/\/doi.org\/10.1016\/j.compmedimag.2010.09.004","journal-title":"Comput Med Imaging Graph"},{"issue":"1","key":"12642_CR34","doi-asserted-by":"publisher","first-page":"89","DOI":"10.14257\/ijgdc.2018.11.1.09","volume":"11","author":"S Dutta","year":"2018","unstructured":"Dutta S, Manideep BCS, Basha SM, Caytiles RD, Iyengar NCSN (2018) Classification of diabetic retinopathy images by using deep learning models. Int J Grid Distrib Comput 11(1):89\u2013106. https:\/\/doi.org\/10.14257\/ijgdc.2018.11.1.09","journal-title":"Int J Grid Distrib Comput"},{"issue":"5","key":"12642_CR35","doi-asserted-by":"publisher","first-page":"741","DOI":"10.1016\/S0161-6420(13)38009-9","volume":"98","author":"Early Treatment Diabetic Retinopathy Study Design and Baseline Patient Characteristics: ETDRS Report Number 7","year":"1991","unstructured":"Early Treatment Diabetic Retinopathy Study Design and Baseline Patient Characteristics: ETDRS Report Number 7 (1991) Early treatment diabetic retinopathy study research group. Ophthalmology. 98(5):741\u2013756. https:\/\/doi.org\/10.1016\/S0161-6420(13)38009-9","journal-title":"Ophthalmology."},{"issue":"1","key":"12642_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12938-019-0675-9","volume":"18","author":"N Eftekhari","year":"2019","unstructured":"Eftekhari N, Pourreza HR, Masoudi M, Ghiasi-Shirazi K, Saeedi E (2019) Microaneurysm detection in fundus images using a two-step convolutional neural network. Biomed Eng Online 18(1):1\u201316. https:\/\/doi.org\/10.1186\/s12938-019-0675-9","journal-title":"Biomed Eng Online"},{"issue":"3","key":"12642_CR37","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1016\/S0169-2607(00)00065-1","volume":"62","author":"BM Ege","year":"2000","unstructured":"Ege BM, Hejlesen OK, Larsen OV, M\u00f8ller K, Jennings B, Kerr D, Cavan DA (2000) Screening for diabetic retinopathy using computer based image analysis and statistical classification. Comput Methods Prog Biomed 62(3):165\u2013175. https:\/\/doi.org\/10.1016\/S0169-2607(00)00065-1","journal-title":"Comput Methods Prog Biomed"},{"key":"12642_CR38","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1016\/j.compbiomed.2010.05.004","volume":"40","author":"MHA Fadzil","year":"2010","unstructured":"Fadzil MHA, Izhar LI, Nugroho H, Nugroho HA (2010) Determination of foveal avascular zone in diabetic retinopathy digital fundus images. Comput Biol Med 40:657\u2013664. https:\/\/doi.org\/10.1016\/j.compbiomed.2010.05.004","journal-title":"Comput Biol Med"},{"issue":"6","key":"12642_CR39","doi-asserted-by":"publisher","first-page":"693","DOI":"10.1007\/s11517-011-0734-2","volume":"49","author":"MHA Fadzil","year":"2011","unstructured":"Fadzil MHA, Izhar LI, Nugroho H, Nugroho HA (2011) Analysis of retinal fundus images for grading of diabetic retinopathy severity. Med Biol Eng Comput 49(6):693\u2013700. https:\/\/doi.org\/10.1007\/s11517-011-0734-2","journal-title":"Med Biol Eng Comput"},{"key":"12642_CR40","doi-asserted-by":"publisher","first-page":"84","DOI":"10.2337\/diacare.27.2007.S84","volume":"27","author":"DS Fong","year":"2004","unstructured":"Fong DS, Aiello L, Gardner TW, King GL, Blankenship G, Cavallerano JD, Ferris FL, Klein R (2004) Retinopathy in diabetes. Diabetes Care 27:84\u201387. https:\/\/doi.org\/10.2337\/diacare.27.2007.S84","journal-title":"Diabetes Care"},{"key":"12642_CR41","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.bspc.2017.02.012","volume":"35","author":"MM Fraz","year":"2017","unstructured":"Fraz MM, Jahangir W, Zahid S, Hamayun MM, Barman SA (2017) Multiscale segmentation of exudates in retinal images using contextual cues and ensemble classification. Biomed Signal Process Control 35:50\u201362. https:\/\/doi.org\/10.1016\/j.bspc.2017.02.012","journal-title":"Biomed Signal Process Control"},{"issue":"5","key":"12642_CR42","doi-asserted-by":"publisher","first-page":"823","DOI":"10.1016\/S0161-6420(13)38014-2","volume":"98","author":"Fundus Photographic Risk Factors for Progression of Diabetic Retinopathy: ETDRS Report Number 12","year":"1991","unstructured":"Fundus Photographic Risk Factors for Progression of Diabetic Retinopathy: ETDRS Report Number 12 (1991) Early treatment diabetic retinopathy study research group. Ophthalmology. 98(5):823\u2013833. https:\/\/doi.org\/10.1016\/S0161-6420(13)38014-2","journal-title":"Ophthalmology."},{"issue":"2","key":"12642_CR43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/electronics9020274","volume":"9","author":"TR Gadekallu","year":"2020","unstructured":"Gadekallu TR, Khare N, Bhattacharya S, Singh S, Maddikunta PKR, Ra I, Alazab M (2020) Early detection of diabetic retinopathy using PCA-firefly based deep learning model. Electronics 9(2):1\u201316. https:\/\/doi.org\/10.3390\/electronics9020274","journal-title":"Electronics"},{"issue":"11","key":"12642_CR44","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 (1996) Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool. Br J Ophthalmol 80(11):940\u2013944. https:\/\/doi.org\/10.1136\/bjo.80.11.940","journal-title":"Br J Ophthalmol"},{"issue":"7","key":"12642_CR45","doi-asserted-by":"publisher","first-page":"962","DOI":"10.1016\/j.ophtha.2017.02.008","volume":"124","author":"R Gargeya","year":"2017","unstructured":"Gargeya R, Leng T (2017) Automated identification of diabetic retinopathy using DL. Ophthalmology. 124(7):962\u2013969. https:\/\/doi.org\/10.1016\/j.ophtha.2017.02.008","journal-title":"Ophthalmology."},{"issue":"6","key":"12642_CR46","doi-asserted-by":"publisher","first-page":"1715","DOI":"10.1007\/s12046-015-0411-5","volume":"40","author":"R Geetharamani","year":"2015","unstructured":"Geetharamani R, Balasubramanian L (2015) Automatic segmentation of blood vessels from retinal fundus images through image processing and data mining techniques. Sadhana. 40(6):1715\u20131736","journal-title":"Sadhana."},{"issue":"5\u20136","key":"12642_CR47","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1016\/j.compmedimag.2013.06.002","volume":"37","author":"ME Gegundez-Arias","year":"2013","unstructured":"Gegundez-Arias ME, Marin D, Bravo JM, Suero A (2013) Locating the fovea center position in digital fundus images using thresholding and feature extraction techniques. Comput Med Imaging Graph 37(5\u20136):386\u2013393. https:\/\/doi.org\/10.1016\/j.compmedimag.2013.06.002","journal-title":"Comput Med Imaging Graph"},{"issue":"2","key":"12642_CR48","doi-asserted-by":"publisher","first-page":"282","DOI":"10.1177\/1932296816629491","volume":"10","author":"JKH Goh","year":"2016","unstructured":"Goh JKH, Cheung CY, Sim SS, Tan PC, Tan GSW, Wong TY (2016) Retinal imaging techniques for diabetic retinopathy screening. J Diabetes Sci Technol 10(2):282\u2013294. https:\/\/doi.org\/10.1177\/1932296816629491","journal-title":"J Diabetes Sci Technol"},{"issue":"5","key":"12642_CR49","doi-asserted-by":"publisher","first-page":"786","DOI":"10.1016\/S0161-6420(13)38012-9","volume":"98","author":"Grading Diabetic Retinopathy from Stereoscopic Color Fundus Photographs\u2014An Extension of the Modified Airlie House Classification: ETDRS Report Number 10","year":"1991","unstructured":"Grading Diabetic Retinopathy from Stereoscopic Color Fundus Photographs\u2014An Extension of the Modified Airlie House Classification: ETDRS Report Number 10 (1991) Early treatment diabetic retinopathy study research group. Ophthalmology. 98(5):786\u2013806. https:\/\/doi.org\/10.1016\/S0161-6420(13)38012-9","journal-title":"Ophthalmology."},{"issue":"22","key":"12642_CR50","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, Kim R, Raman R, Nelson PC, Mega JL, Webster DR (2016) Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. Jama. 316(22):2402\u20132410. https:\/\/doi.org\/10.1001\/jama.2016.17216","journal-title":"Jama."},{"key":"12642_CR51","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1016\/j.imu.2017.05.006","volume":"9","author":"MM Habib","year":"2017","unstructured":"Habib MM, Welikala RA, Hoppe A, Owen CG, Rudnicka AR, Barman SA (2017) Detection of microaneurysms in retinal images using an ensemble classifier. Inf Med Unlocked 9:44\u201357. https:\/\/doi.org\/10.1016\/j.imu.2017.05.006","journal-title":"Inf Med Unlocked"},{"key":"12642_CR52","doi-asserted-by":"publisher","unstructured":"Hani AFM, Ngah NF, George TM, Izhar LI, Nugroho H, Nugroho HA (2010) Analysis of foveal avascular zone in colour fundus images for grading of diabetic retinopathy severity. 32nd annual international conference of the IEEE EMBS Buenos Aires, Argentina. 5632-5635. https:\/\/doi.org\/10.1109\/IEMBS.2010.5628041","DOI":"10.1109\/IEMBS.2010.5628041"},{"key":"12642_CR53","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.compbiomed.2015.07.002","volume":"65","author":"B Harangi","year":"2015","unstructured":"Harangi B, Hajdu A (2015) Detection of the optic disc in fundus images by combining probability models. Comput Biol Med 65:10\u201324. https:\/\/doi.org\/10.1016\/j.compbiomed.2015.07.002","journal-title":"Comput Biol Med"},{"issue":"1","key":"12642_CR54","doi-asserted-by":"publisher","first-page":"50","DOI":"10.14456\/mijet.2021.8","volume":"7","author":"T Hattiya","year":"2021","unstructured":"Hattiya T, Dittakan K, Musikasuwan S (2021) Diabetic retinopathy detection using convolutional neural network: a comparative study on different architectures. Mahasarakham international journal of engineering. Technology 7(1):50\u201360. https:\/\/doi.org\/10.14456\/mijet.2021.8","journal-title":"Technology"},{"issue":"12","key":"12642_CR55","doi-asserted-by":"publisher","first-page":"10600","DOI":"10.1016\/j.eswa.2012.02.157","volume":"39","author":"HK Hsiao","year":"2012","unstructured":"Hsiao HK, Liu CC, Yu CY, Kuo SW, Yu SS (2012) A novel optic disc detection scheme on retinal images. Expert Syst Appl 39(12):10600\u201310606. https:\/\/doi.org\/10.1016\/j.eswa.2012.02.157","journal-title":"Expert Syst Appl"},{"key":"12642_CR56","doi-asserted-by":"crossref","unstructured":"Huang G, Liu S, Maaten L, Weinberger KQ (2018) CondenseNet: an efficient DenseNet using learned group convolutions. In proceedings of the IEEE conference on computer vision and pattern recognition. 2752-2761","DOI":"10.1109\/CVPR.2018.00291"},{"key":"12642_CR57","unstructured":"Islam SMS, Hasan MM, Abdullah S (2018) Deep learning based early detection and grading of diabetic retinopathy using retinal fundus images. arXiv preprint arXiv:1812.10595v1."},{"issue":"9","key":"12642_CR58","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/s20092559","volume":"20","author":"C Iwendi","year":"2020","unstructured":"Iwendi C, Khan S, Anajemba JH, Mittal M, Alenezi M, Alazab M (2020) The use of ensemble models for multiple class and binary class classification for improving intrusion detection systems. Sensors 20(9):1\u201337. https:\/\/doi.org\/10.3390\/s20092559","journal-title":"Sensors"},{"key":"12642_CR59","doi-asserted-by":"publisher","unstructured":"Jelinek HF, Pires R, Padilha R, Goldenstein S, Wainer J, Bossomaier T, Rocha A (2012) Data fusion for multi-lesion diabetic retinopathy detection. 25th IEEE international symposium on computer-based medical systems (CBMS), pp. 1-4. https:\/\/doi.org\/10.1109\/CBMS.2012.6266342","DOI":"10.1109\/CBMS.2012.6266342"},{"issue":"3","key":"12642_CR60","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/a12030051","volume":"12","author":"Q Ji","year":"2019","unstructured":"Ji Q, Huang J, He W, Sun Y (2019) Optimized deep convolutional neural networks for identification of macular diseases from optical coherence tomography images. Algorithms 12(3):1\u201312. https:\/\/doi.org\/10.3390\/a12030051","journal-title":"Algorithms"},{"key":"12642_CR61","doi-asserted-by":"publisher","unstructured":"Jiang X, Xiang D, Zhang B, Zhu W, Shi F and Chen X (2016) Automatic Co-segmentation of Lung Tumor based on Random forest in PET-CT Images. Medical Imaging 2016: Image processing. SPIE Medical Imaging https:\/\/doi.org\/10.1117\/12.2216361","DOI":"10.1117\/12.2216361"},{"issue":"6","key":"12642_CR62","doi-asserted-by":"publisher","first-page":"1395","DOI":"10.1109\/TMI.2015.2512606","volume":"35","author":"C Jin","year":"2016","unstructured":"Jin C, Shi F, Xiang D, Jiang X, Zhang B, Wang X, Zhu W, Gao E, Chen X (2016) 3D fast automatic segmentation of kidney based on modified AAM and random Forest. IEEE Trans Med Imaging 35(6):1395\u20131407. https:\/\/doi.org\/10.1109\/TMI.2015.2512606","journal-title":"IEEE Trans Med Imaging"},{"issue":"5","key":"12642_CR63","doi-asserted-by":"publisher","first-page":"21","DOI":"10.5121\/ijcsit.2013.5502","volume":"5","author":"SB Junior","year":"2013","unstructured":"Junior SB, Welfer D (2013) Automatic detection of microaneurysms and hemorrhages in color eye fundus images. Int J Comput Sci Inf Technol 5(5):21\u201337. https:\/\/doi.org\/10.5121\/ijcsit.2013.5502","journal-title":"Int J Comput Sci Inf Technol"},{"issue":"3","key":"12642_CR64","doi-asserted-by":"publisher","first-page":"1002","DOI":"10.17148\/IJARCCE.2017.63233","volume":"6","author":"P Kale","year":"2017","unstructured":"Kale P, Janwe N (2017) Detection of retinal hemorrhage in color fundus image. Int J Adv Res Comput Commun Eng 6(3):1002\u20131005. https:\/\/doi.org\/10.17148\/IJARCCE.2017.63233","journal-title":"Int J Adv Res Comput Commun Eng"},{"key":"12642_CR65","doi-asserted-by":"publisher","first-page":"959","DOI":"10.1007\/s11760-020-01820-2","volume":"15","author":"KC Kamal","year":"2021","unstructured":"Kamal KC, Yin Z, Wu M, Wu Z (2021) Evaluation of deep learning-based approaches for COVID-19 classification based on chest X-ray images. SIViP 15:959\u2013966. https:\/\/doi.org\/10.1007\/s11760-020-01820-2","journal-title":"SIViP"},{"key":"12642_CR66","doi-asserted-by":"publisher","unstructured":"Kamble R, Kokare M (2017) Detection of microaneurysms using local rank transform in color fundus images. 2017 IEEE international conference on image processing. Pp. 4442\u20144446. https:\/\/doi.org\/10.1109\/ICIP.2017.8297122","DOI":"10.1109\/ICIP.2017.8297122"},{"key":"12642_CR67","doi-asserted-by":"publisher","first-page":"382","DOI":"10.1016\/j.compbiomed.2017.04.016","volume":"87","author":"R Kamble","year":"2017","unstructured":"Kamble R, Kokare M, Deshmukh G, Hussain FA, Meriaudeau F (2017) Localization of optic disc and fovea in retinal images using intensity based line scanning analysis. Comput Biol Med 87:382\u2013396. https:\/\/doi.org\/10.1016\/j.compbiomed.2017.04.016","journal-title":"Comput Biol Med"},{"key":"12642_CR68","doi-asserted-by":"publisher","unstructured":"Kamel M, Belkassim S, Mendonca AM, Campilho A (2001) A neural network approach for the automatic detection of microaneurysms in retinal angiograms. International joint conference on neural networks. Proceedings, 4: 2695-2699. https:\/\/doi.org\/10.1109\/IJCNN.2001.938798","DOI":"10.1109\/IJCNN.2001.938798"},{"issue":"2","key":"12642_CR69","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/j.cmpb.2014.08.003","volume":"117","author":"EF Kao","year":"2014","unstructured":"Kao EF, Lin PC, Chou MC, Jaw TS, Liu GC (2014) Automated detection of fovea in fundus images based on vessel-free zone and adaptive Gaussian template. Comput Methods Prog Biomed 117(2):92\u2013103. https:\/\/doi.org\/10.1016\/j.cmpb.2014.08.003","journal-title":"Comput Methods Prog Biomed"},{"key":"12642_CR70","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.1016\/j.cell.2018.02.010","volume":"172","author":"DS Kermany","year":"2018","unstructured":"Kermany DS, Goldbaum M, Cai W, Valentim CCS, Liang H, Baxter SL, McKeown A, Yang G, Wu X, Yan F, Dong J, Prasadha MK, Pei J, Ting MYL, Zhu J, Li C, Hewett S, Dong J, Ziyar I, \u2026 Zhang K (2018) Identifying medical diagnoses and treatable diseases by image-based deep Learning. Cell 172:1122\u20131131. https:\/\/doi.org\/10.1016\/j.cell.2018.02.010","journal-title":"Cell"},{"key":"12642_CR71","doi-asserted-by":"publisher","unstructured":"Khojasteh P, Aliahmad B, Kumar DK (2018) Fundus images analysis using deep features for detection of exudates, hemorrhages and microaneurysms. BMC Ophthalmology.1-13. 18. https:\/\/doi.org\/10.1186\/s12886-018-0954-4","DOI":"10.1186\/s12886-018-0954-4"},{"key":"12642_CR72","unstructured":"Kirkpatrick C (2013) Intraretinal microvascular abnormality (IrMA). EyeRounds online atlas of ophthalmology. Available at https:\/\/webeye.ophth.uiowa.edu\/eyeforum\/atlas\/pages\/IRMA.htm, accessed on 27-05-2020"},{"key":"12642_CR73","unstructured":"Kokame GT, Lai JC (2012) Intraretinal microvascular abnormalities, retina image Bank, American Society of Retina Specialists Available at: https:\/\/imagebank.asrs.org\/file\/1361\/intraretinal-microvascular-abnormalities, accessed on 14-05-2020"},{"issue":"2","key":"12642_CR74","doi-asserted-by":"publisher","first-page":"47","DOI":"10.17305\/bjbms.2006.3171","volume":"6","author":"I Kulenovic","year":"2006","unstructured":"Kulenovic I, Rasic S, Karcic S (2006) Development of microvascular complications in type 1 diabetic patients 10 years follow-up. Bosnian J Basic Med Sci 6(2):47\u201350. https:\/\/doi.org\/10.17305\/bjbms.2006.3171","journal-title":"Bosnian J Basic Med Sci"},{"key":"12642_CR75","doi-asserted-by":"publisher","first-page":"486","DOI":"10.1016\/j.procs.2016.07.237","volume":"93","author":"PNS Kumar","year":"2016","unstructured":"Kumar PNS, Deepak RU, Sathar A, Sahasranamam V, Kumar RR (2016) Automated detection system for diabetic retinopathy using two field fundus photography. Procedia Comput Sci 93:486\u2013494. https:\/\/doi.org\/10.1016\/j.procs.2016.07.237","journal-title":"Procedia Comput Sci"},{"key":"12642_CR76","doi-asserted-by":"publisher","unstructured":"Lachure J, Deorankar AV, Lachure S, Gupta S, Jadhav R (2015) Diabetic retinopathy using morphological operations and machine learning. In 2015 IEEE international advance computing conference (IACC). 617-622. https:\/\/doi.org\/10.1109\/IADCC.2015.7154781","DOI":"10.1109\/IADCC.2015.7154781"},{"issue":"1","key":"12642_CR77","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 (2018) Retinal lesion detection with deep learning using image patches. Multidisciplinary Ophthalmic Imaging 59(1):590\u2013596. https:\/\/doi.org\/10.1167\/iovs.17-22721","journal-title":"Multidisciplinary Ophthalmic Imaging"},{"key":"12642_CR78","unstructured":"Lam C, Yi D, Guo M, Lindsey T (2018) Automated Detection of Diabetic Retinopathy using DL. AMIA Summits on Translational Science Proceedings.147\u2013155. PMID: 29888061; PMCID: PMC5961805."},{"issue":"2","key":"12642_CR79","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1109\/TMI.2012.2228665","volume":"32","author":"I Lazar","year":"2013","unstructured":"Lazar I, Hajdu A (2013) Retinal microaneurysm detection through local rotating cross-section profile analysis. IEEE Trans Med Imaging 32(2):400\u2013407. https:\/\/doi.org\/10.1109\/TMI.2012.2228665","journal-title":"IEEE Trans Med Imaging"},{"issue":"4","key":"12642_CR80","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1097\/IJG.0000000000001458","volume":"29","author":"J Lee","year":"2020","unstructured":"Lee J, Kim YK, Park KH, Jeoung JW (2020) Diagnosing Glaucoma with spectral-domain optical coherence tomography using deep learning classifier. J Glaucoma 29(4):287\u2013294. https:\/\/doi.org\/10.1097\/IJG.0000000000001458","journal-title":"J Glaucoma"},{"issue":"4","key":"12642_CR81","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1007\/s11892-013-0393-9","volume":"13","author":"B Li","year":"2013","unstructured":"Li B, Li HK (2013) Automated analysis of diabetic retinopathy images: principles, recent developments, and emerging trends. Curr Diab Rep 13(4):453\u2013459. https:\/\/doi.org\/10.1007\/s11892-013-0393-9","journal-title":"Curr Diab Rep"},{"key":"12642_CR82","doi-asserted-by":"publisher","unstructured":"Li C, Kao CY, Gore JC, Ding Z (2007) Implicit active contours driven by local binary fitting energy. 2007 IEEE conference on computer vision and pattern Recognition.1-7.https:\/\/doi.org\/10.1109\/CVPR.2007.383014","DOI":"10.1109\/CVPR.2007.383014"},{"key":"12642_CR83","doi-asserted-by":"publisher","unstructured":"Li Y, Yeh N, Chen S, and Chung Y (2019) Computer-assisted diagnosis for diabetic retinopathy based on fundus images using deep convolutional neural network. Mob Inf Syst 1-14. https:\/\/doi.org\/10.1155\/2019\/6142839.","DOI":"10.1155\/2019\/6142839"},{"issue":"11","key":"12642_CR84","doi-asserted-by":"publisher","first-page":"2369","DOI":"10.1109\/TMI.2016.2546227","volume":"35","author":"P Liskowski","year":"2015","unstructured":"Liskowski P, Krawiec K (2015) Segmenting retinal blood vessels with deep neural networks. IEEE Trans Med Imaging 35(11):2369\u20132380. https:\/\/doi.org\/10.1109\/TMI.2016.2546227","journal-title":"IEEE Trans Med Imaging"},{"issue":"2","key":"12642_CR85","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1109\/MSP.2009.935453","volume":"27","author":"J Ma","year":"2010","unstructured":"Ma J, Plonka G (2010) A review of Curvelets and recent applications. IEEE Signal Process Mag 27(2):118\u2013133","journal-title":"IEEE Signal Process Mag"},{"key":"12642_CR86","doi-asserted-by":"publisher","unstructured":"Manjaramkar A, Kokare M (2017) Statistical Geometrical Features for Microaneurysm Detection. J Digit Imaging 31:224\u2013234. https:\/\/doi.org\/10.1007\/s10278-017-0008-0","DOI":"10.1007\/s10278-017-0008-0"},{"issue":"12","key":"12642_CR87","doi-asserted-by":"publisher","first-page":"2811","DOI":"10.1063\/1.4981966","volume":"5","author":"KS Mann","year":"2017","unstructured":"Mann KS, Kaur S (2017) Segmentation of retinal blood vessels using optimized features for detection of diabetic retinopathy. Int J Res Appl Sci Eng Technol 5(12):2811\u20132821. https:\/\/doi.org\/10.1063\/1.4981966","journal-title":"Int J Res Appl Sci Eng Technol"},{"key":"12642_CR88","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1109\/RBME.2017.2705064","volume":"10","author":"RF Mansour","year":"2017","unstructured":"Mansour RF (2017) Evolutionary computing enriched computer aided diagnosis system for diabetic retinopathy: a survey. IEEE Rev Biomed Eng 10:334\u2013349. https:\/\/doi.org\/10.1109\/RBME.2017.2705064","journal-title":"IEEE Rev Biomed Eng"},{"issue":"1","key":"12642_CR89","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1007\/s13534-017-0047-y","volume":"8","author":"RF Mansour","year":"2018","unstructured":"Mansour RF (2018) Deep-learning-based automatic computer-aided diagnosis system for diabetic retinopathy. Biomed Eng Lett 8(1):41\u201357. https:\/\/doi.org\/10.1007\/s13534-017-0047-y","journal-title":"Biomed Eng Lett"},{"key":"12642_CR90","doi-asserted-by":"publisher","unstructured":"Massey EM, Hunter A (2011) Augmenting the classification of retinal lesions using spatial distribution. 33rd annual international conference of the IEEE EMBS Boston. 3967-3970. https:\/\/doi.org\/10.1109\/IEMBS.2011.6090985","DOI":"10.1109\/IEMBS.2011.6090985"},{"key":"12642_CR91","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.compbiomed.2016.04.007","volume":"74","author":"JP Medhi","year":"2016","unstructured":"Medhi JP, Dandapat S (2016) An effective fovea detection and automatic assessment of diabetic maculopathy in color fundus images. Comput Biol Med 74:30\u201344. https:\/\/doi.org\/10.1016\/j.compbiomed.2016.04.007","journal-title":"Comput Biol Med"},{"key":"12642_CR92","doi-asserted-by":"publisher","first-page":"713","DOI":"10.1007\/s40846-018-0454-2","volume":"39","author":"N Memari","year":"2018","unstructured":"Memari N, Ramli AR, Saripan MIB, Mashohor S, Moghbel M (2018) Retinal blood vessel segmentation by using matched filtering and fuzzy C-means clustering with integrated level set method for diabetic retinopathy assessment. J Med Biol Eng 39:713\u2013731. https:\/\/doi.org\/10.1007\/s40846-018-0454-2","journal-title":"J Med Biol Eng"},{"issue":"3","key":"12642_CR93","first-page":"143","volume":"2","author":"SP Meshram","year":"2013","unstructured":"Meshram SP, Pawar MS (2013) Extraction of retinal blood vessels from diabetic retinopathy imagery using contrast limited adaptive histogram equalization. Int J Adv Comput Theory Eng 2(3):143\u2013147","journal-title":"Int J Adv Comput Theory Eng"},{"key":"12642_CR94","unstructured":"Microaneyrysms, The COMS Grading Scheme: Graded Features, University of IOWA Health Care, Department of Ophthalmology and Visual Sciences, Available at http:\/\/webeye.ophth.uiowa.edu\/dept\/coms\/grading\/images\/11-mircoaneurysms.jpg Accessed on 13-07-2020"},{"key":"12642_CR95","doi-asserted-by":"publisher","unstructured":"Mizutani A, Muramatsu C, Hatanaka Y, Suemori S, Hara T, Fujita H (2009) Automated microaneurysm detection method based on double-ring filter in retinal fundus images. Proceedings of SPIE 7260. Medical imaging 2009: computer-aided diagnosis. 72-78. https:\/\/doi.org\/10.1117\/12.813468","DOI":"10.1117\/12.813468"},{"key":"12642_CR96","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.knosys.2012.09.008","volume":"39","author":"MRK Mookiah","year":"2013","unstructured":"Mookiah MRK, Acharya UR, Martis RJ, Chua CK, Lim CM, Ng EYK, Laude A (2013) Evolutionary algorithm based classifier parameter tuning for automatic diabetic retinopathy grading: a hybrid feature extraction approach. Knowl-Based Syst 39:9\u201322. https:\/\/doi.org\/10.1016\/j.knosys.2012.09.008","journal-title":"Knowl-Based Syst"},{"issue":"5","key":"12642_CR97","doi-asserted-by":"publisher","first-page":"584","DOI":"10.1109\/TMI.2005.843738","volume":"24","author":"M Niemeijer","year":"2005","unstructured":"Niemeijer M, Van Ginneken B, Staal J, Suttorp-Schulten MSA, Abramoff MD (2005) Automatic detection of red lesions in digital color fundus photographs. IEEE Trans Med Imaging 24(5):584\u2013592. https:\/\/doi.org\/10.1109\/TMI.2005.843738","journal-title":"IEEE Trans Med Imaging"},{"issue":"3","key":"12642_CR98","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1515\/bmt-2018-0098","volume":"64","author":"M Noor-ul-huda","year":"2019","unstructured":"Noor-ul-huda M, Tehsin S, Ahmed S, Niazi FAK, Murtaza Z (2019) Retinal images benchmark for the detection of diabetic retinopathy and clinically significant macular edema (CSME). Biomed Eng 64(3):297\u2013307. https:\/\/doi.org\/10.1515\/bmt-2018-0098","journal-title":"Biomed Eng"},{"issue":"3\u20138","key":"12642_CR99","first-page":"107","volume":"9","author":"HA Nugroho","year":"2017","unstructured":"Nugroho HA, Purnamasari D, Soesanti I, Oktoeberza WKZ, Dharmawan DA (2017) Segmentation of foveal avascular zone in colour fundus images based on retinal capillary endpoints detection. J Telecom Electron Comput Eng 9(3\u20138):107\u2013112","journal-title":"J Telecom Electron Comput Eng"},{"key":"12642_CR100","doi-asserted-by":"publisher","unstructured":"Oliveira WS, Teixeira JV, Ren TI, Cavalcanti GDC, Sijbers J (2016) Unsupervised retinal vessel segmentation using combined filters. PLoS One 1-21. 11. https:\/\/doi.org\/10.1371\/journal.pone.0149943","DOI":"10.1371\/journal.pone.0149943"},{"key":"12642_CR101","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 (2018) An ensemble deep learning based approach for red lesion detection in fundus images. Comput Methods Prog Biomed 153:115\u2013127. https:\/\/doi.org\/10.1016\/j.cmpb.2017.10.017","journal-title":"Comput Methods Prog Biomed"},{"issue":"5","key":"12642_CR102","first-page":"11","volume":"6","author":"MB Patwari","year":"2013","unstructured":"Patwari MB, Manza RR, Rajput YM, Saswade M, Deshpande N (2013) Detection and counting the microaneurysms using image processing techniques. Int J Appl Inf Syst 6(5):11\u201317","journal-title":"Int J Appl Inf Syst"},{"key":"12642_CR103","doi-asserted-by":"publisher","unstructured":"Pentland BT, Liu P, Kremser W, Haerem T (2020) The dynamics of drift in digitized processes. MIS quarterly. 44(1): 19\u201347. https:\/\/doi.org\/10.25300\/MISQ\/2020\/14458","DOI":"10.25300\/MISQ\/2020\/14458"},{"issue":"3","key":"12642_CR104","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1016\/j.artmed.2013.12.005","volume":"60","author":"C Pereira","year":"2014","unstructured":"Pereira C, Veiga D, Mahdjoub J, Guessoum Z, Gon\u00e7alves L, Ferreira M, Monteiro J (2014) Using a multi-agent system approach for microaneurysm detection in fundus images. Artif Intell Med 60(3):179\u2013188. https:\/\/doi.org\/10.1016\/j.artmed.2013.12.005","journal-title":"Artif Intell Med"},{"key":"12642_CR105","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.ins.2014.10.059","volume":"296","author":"C Pereira","year":"2015","unstructured":"Pereira C, Gon\u00e7alves L, Ferreira M (2015) Exudate segmentation in fundus images using an ant colony optimization approach. Inf Sci 296:14\u201324. https:\/\/doi.org\/10.1016\/j.ins.2014.10.059","journal-title":"Inf Sci"},{"issue":"3","key":"12642_CR106","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 (2018) Indian diabetic retinopathy image dataset (IDRiD): a database for diabetic retinopathy screening research. Data. 3(3):25. https:\/\/doi.org\/10.3390\/data3030025","journal-title":"Data."},{"key":"12642_CR107","doi-asserted-by":"publisher","first-page":"136668","DOI":"10.1109\/ACCESS.2020.3005044","volume":"8","author":"AM Pour","year":"2020","unstructured":"Pour AM, Seyedarabi H, Jahromi SHA, Javadzadeh A (2020) Automatic detection and monitoring of diabetic retinopathy using efficient convolutional neural networks and contrast limited adaptive histogram equalization. IEEE Access 8:136668\u2013136673. https:\/\/doi.org\/10.1109\/ACCESS.2020.3005044","journal-title":"IEEE Access"},{"key":"12642_CR108","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1016\/j.procs.2016.07.014","volume":"90","author":"H Pratt","year":"2016","unstructured":"Pratt H, Coenen F, Broadbent DM, Harding SP, Zheng Y (2016) Convolutional neural networks for diabetic retinopathy. Procedia Comput Sci 90:200\u2013205. https:\/\/doi.org\/10.1016\/j.procs.2016.07.014","journal-title":"Procedia Comput Sci"},{"key":"12642_CR109","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1016\/j.cmpb.2016.09.018","volume":"137","author":"P Prenta\u0161i\u0107","year":"2016","unstructured":"Prenta\u0161i\u0107 P, Lon\u010dari\u0107 S (2016) Detection of exudates in fundus photographs using deep neural networks and anatomical landmark detection fusion. Comput Methods Prog Biomed 137:281\u2013292. https:\/\/doi.org\/10.1016\/j.cmpb.2016.09.018","journal-title":"Comput Methods Prog Biomed"},{"key":"12642_CR110","unstructured":"Proliferative Diabetic Retinopathy - Optic Disc Neovascularization - Post Intravitreal Avastin Injections, The Retina Reference, Available at - http:\/\/www.retinareference.com\/diseases\/beb00894be590ec0\/images\/46019c8a9e\/, Accessed on 23-11-2020"},{"issue":"9","key":"12642_CR111","doi-asserted-by":"publisher","first-page":"1230","DOI":"10.1109\/TMI.2008.920619","volume":"27","author":"G Quellec","year":"2008","unstructured":"Quellec G, Lamard M, Josselin PM, Cazuguel G, Cochener B, Roux C (2008) Optimal wavelet transform for the detection of microaneurysms in retina photographs. IEEE Trans Med Imaging 27(9):1230\u20131241. https:\/\/doi.org\/10.1109\/TMI.2008.920619","journal-title":"IEEE Trans Med Imaging"},{"key":"12642_CR112","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 (2017) Deep image Mining for Diabetic Retinopathy Screening. Med Image Anal 39:178\u2013193. https:\/\/doi.org\/10.1016\/j.media.2017.04.012","journal-title":"Med Image Anal"},{"issue":"1","key":"12642_CR113","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1016\/j.cviu.2011.09.001","volume":"116","author":"RJ Qureshi","year":"2012","unstructured":"Qureshi RJ, Kovacs L, Harangi B, Nagy B, Peto T, Hajdu A (2012) Combining algorithms for automatic detection of optic disc and macula in fundus images. Comput Vis Image Underst 116(1):138\u2013145. https:\/\/doi.org\/10.1016\/j.cviu.2011.09.001","journal-title":"Comput Vis Image Underst"},{"key":"12642_CR114","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.imu.2020.100360","volume":"19","author":"M Rahimzadeh","year":"2020","unstructured":"Rahimzadeh M, Attar A (2020) A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2. Inf Med unlocked 19:1\u20139. https:\/\/doi.org\/10.1016\/j.imu.2020.100360","journal-title":"Inf Med unlocked"},{"issue":"1","key":"12642_CR115","first-page":"13","volume":"36","author":"DSS Raja","year":"2015","unstructured":"Raja DSS, Vasuki S (2015) Screening diabetic retinopathy in developing countries using retinal images. Appl Med Inf 36(1):13\u201322","journal-title":"Appl Med Inf"},{"key":"12642_CR116","first-page":"1","volume":"975","author":"YM Rajput","year":"2015","unstructured":"Rajput YM, Manza RR, Patwari MB (2015) Extraction of cotton wool spot using multi resolution analysis and classification using K-means clustering. Int J Comput Appl 975:1\u20135","journal-title":"Int J Comput Appl"},{"key":"12642_CR117","doi-asserted-by":"publisher","unstructured":"Rakhlin A (2017) Diabetic retinopathy detection through integration of deep learning classification framework. bioRxiv, p.225508. https:\/\/doi.org\/10.1101\/225508","DOI":"10.1101\/225508"},{"key":"12642_CR118","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. Pp. 210-217. https:\/\/doi.org\/10.1109\/CVPR.2009.5206763","DOI":"10.1109\/CVPR.2009.5206763"},{"key":"12642_CR119","unstructured":"Retinal blood vessels, IMAIOS, Available at - https:\/\/www.imaios.com\/en\/e-Anatomy\/Anatomical-Parts\/Retinal-blood-vessels, Accessed on 19-06-2020"},{"key":"12642_CR120","doi-asserted-by":"publisher","first-page":"274","DOI":"10.1016\/j.bspc.2018.05.027","volume":"45","author":"MN Reza","year":"2018","unstructured":"Reza MN (2018) Automatic detection of optic disc in color fundus retinal images using circle operator. Biomed Signal Process Control 45:274\u2013283. https:\/\/doi.org\/10.1016\/j.bspc.2018.05.027","journal-title":"Biomed Signal Process Control"},{"key":"12642_CR121","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.compmedimag.2015.07.001","volume":"44","author":"R Rosas-Romero","year":"2015","unstructured":"Rosas-Romero R, Mart\u00ednez-Carballido J, Hern\u00e1ndez-Capistr\u00e1n J, Uribe-Valencia LJ (2015) A method to assist in the diagnosis of early diabetic retinopathy: image processing applied to detection of microaneurysms in fundus images. Comput Med Imaging Graph 44:41\u201353. https:\/\/doi.org\/10.1016\/j.compmedimag.2015.07.001","journal-title":"Comput Med Imaging Graph"},{"key":"12642_CR122","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1016\/j.patrec.2020.04.026","volume":"135","author":"A Samanta","year":"2020","unstructured":"Samanta A, Saha A, Satapathy SC, Fernandes SL, Zhang Y (2020) Automated detection of diabetic retinopathy using convolutional neural networks on a small dataset. Pattern Recogn Lett 135:293\u2013298. https:\/\/doi.org\/10.1016\/j.patrec.2020.04.026","journal-title":"Pattern Recogn Lett"},{"issue":"3","key":"12642_CR123","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1109\/JPROC.2021.3060483","volume":"109","author":"W Samek","year":"2021","unstructured":"Samek W, Montavon G, Lapuschkin S, Anders CJ, M\u00fcller K (2021) Explaining deep neural networks and beyond: a review of methods and applications. Proc IEEE 109(3):247\u2013278. https:\/\/doi.org\/10.1109\/JPROC.2021.3060483","journal-title":"Proc IEEE"},{"key":"12642_CR124","doi-asserted-by":"publisher","unstructured":"Sarki R, Michalska S, Ahmed K, Wang H, Zhang Y (2019) Convolutional neural networks for mild diabetic retinopathy detection: an experimental study. bioRxiv. 1-18. https:\/\/doi.org\/10.1101\/763136","DOI":"10.1101\/763136"},{"issue":"4","key":"12642_CR125","doi-asserted-by":"publisher","first-page":"1116","DOI":"10.1109\/TMI.2015.2509785","volume":"35","author":"L Seoud","year":"2015","unstructured":"Seoud L, Hurtut T, Chelbi J, Cheriet F, Langlois JMP (2015) Red lesion detection using dynamic shape features for diabetic retinopathy screening. IEEE Trans Med Imaging 35(4):1116\u20131126. https:\/\/doi.org\/10.1109\/TMI.2015.2509785","journal-title":"IEEE Trans Med Imaging"},{"issue":"2","key":"12642_CR126","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 (2002) Automated detection of diabetic retinopathy on digital fundus images. Diabet Med 19(2):105\u2013112. https:\/\/doi.org\/10.1046\/j.1464-5491.2002.00613.x","journal-title":"Diabet Med"},{"key":"12642_CR127","doi-asserted-by":"publisher","unstructured":"Soniya, Paul S, Singh L (2016) Heterogeneous modular deep neural network for diabetic retinopathy detection. In 2016 IEEE Region 10 Humanitarian Technology Conference (R10-HTC). pp. 1\u20136. https:\/\/doi.org\/10.1109\/R10-HTC.2016.7906821","DOI":"10.1109\/R10-HTC.2016.7906821"},{"issue":"3","key":"12642_CR128","doi-asserted-by":"publisher","first-page":"2148","DOI":"10.3390\/s90302148","volume":"9","author":"A Sopharak","year":"2009","unstructured":"Sopharak A, Uyyanonvara B, Barman S (2009) Automatic exudate detection from non-dilated diabetic retinopathy retinal images using fuzzy C-means clustering. Sensors. 9(3):2148\u20132161. https:\/\/doi.org\/10.3390\/s90302148","journal-title":"Sensors."},{"issue":"3","key":"12642_CR129","first-page":"295","volume":"38","author":"A Sopharak","year":"2011","unstructured":"Sopharak A, Uyyanonvara B, Barman S (2011) Automatic microaneurysm detection from non-dilated diabetic retinopathy retinal images using mathematical morphology methods. IAENG Int J Comput Sci 38(3):295\u2013301","journal-title":"IAENG Int J Comput Sci"},{"issue":"4","key":"12642_CR130","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1006\/cbmr.1996.0021","volume":"29","author":"T Spencer","year":"1996","unstructured":"Spencer T, Olson JA, McHardy KC, Sharp PF, Forrester JV (1996) An image-processing strategy for the segmentation and quantification of microaneurysms in fluorescein angiograms of the ocular fundus. Comput Biomed Res 29(4):284\u2013302. https:\/\/doi.org\/10.1006\/cbmr.1996.0021","journal-title":"Comput Biomed Res"},{"key":"12642_CR131","unstructured":"Stewart JM, Coassin M, Schwartz DM. Diabetic Retinopathy. (2017) In: Feingold KR, Anawalt B, Boyce A, et al., editors. Endotext [Internet]. South Dartmouth (MA): MDText.com, Inc.; 2000-. Figure 8, [Active neovascularization in PDR. Fibrovascular...]. Available from: https:\/\/www.ncbi.nlm.nih.gov\/books\/NBK278967\/figure\/diab-retinopathy_f_diab-retinopathy_etx-dm-ch29-fig 8\/ Accessed on 27-05-2020"},{"issue":"6","key":"12642_CR132","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0179790","volume":"12","author":"H Takahashi","year":"2017","unstructured":"Takahashi H, Tampo H, Arai Y, Inoue Y, Kawashima H (2017) Applying artificial intelligence to disease staging: deep learning for improved staging of diabetic retinopathy. PLoS One 12(6):1\u201311. https:\/\/doi.org\/10.1371\/journal.pone.0179790","journal-title":"PLoS One"},{"issue":"12","key":"12642_CR133","doi-asserted-by":"publisher","first-page":"1729","DOI":"10.1109\/TMI.2007.902801","volume":"26","author":"KW Tobin","year":"2007","unstructured":"Tobin KW, Chaum E, Govindasamy VP, Karnowski TP (2007) Detection of anatomic structures in human retinal imagery. IEEE Trans Med Imaging 26(12):1729\u20131739. https:\/\/doi.org\/10.1109\/TMI.2007.902801","journal-title":"IEEE Trans Med Imaging"},{"key":"12642_CR134","doi-asserted-by":"crossref","unstructured":"Tymchenko B, Marchenko P, Spodarets D (2020) Deep Learning Approach to Diabetic Retinopathy Detection. arXiv preprint arXiv:2003.02261.1\u20139.","DOI":"10.5220\/0008970805010509"},{"issue":"1","key":"12642_CR135","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1046\/j.1464-5491.2003.01085.x","volume":"21","author":"D Usher","year":"2003","unstructured":"Usher D, Dumskyj M, Himaga M, Williamson TH, Nussey S, Boyce J (2003) Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening. Diabet Med 21(1):84\u201390. https:\/\/doi.org\/10.1046\/j.1464-5491.2003.01085.x","journal-title":"Diabet Med"},{"issue":"5","key":"12642_CR136","doi-asserted-by":"publisher","first-page":"1273","DOI":"10.1109\/TMI.2016.2526689","volume":"35","author":"MJJP Van Grinsven","year":"2016","unstructured":"Van Grinsven MJJP, Van Ginneken B, Hoyng CB, Theelen T, S\u00e1nchez CI (2016) Fast convolutional neural network training using selective data sampling: application to hemorrhage detection in color fundus images. IEEE Trans Med Imaging 35(5):1273\u20131284. https:\/\/doi.org\/10.1109\/TMI.2016.2526689","journal-title":"IEEE Trans Med Imaging"},{"issue":"10","key":"12642_CR137","doi-asserted-by":"publisher","first-page":"1236","DOI":"10.1109\/TMI.2002.806290","volume":"21","author":"T Walter","year":"2002","unstructured":"Walter T, Klein JC, Massin P, Erginay A (2002) A contribution of image processing to the diagnosis of diabetic retinopathy-detection of exudates in color fundus images of the human retina. IEEE Trans Med Imaging 21(10):1236\u20131243. https:\/\/doi.org\/10.1109\/TMI.2002.806290","journal-title":"IEEE Trans Med Imaging"},{"key":"12642_CR138","unstructured":"Wang Z, Yang J (2018) Diabetic retinopathy detection via deep convolutional networks for discriminative localization and visual explanation. The Workshops of the Thirty-Second AAAI Conference on Artificial Intelligence:514\u2013521"},{"issue":"5","key":"12642_CR139","doi-asserted-by":"publisher","first-page":"1047","DOI":"10.2337\/diacare.27.5.1047","volume":"27","author":"S Wild","year":"2004","unstructured":"Wild S, Roglic G, Green A, Sicree R, King H (2004) Global prevalence of diabetes: estimates for the year 2000 and projections for 2030. Diabetes Care 27(5):1047\u20131053. https:\/\/doi.org\/10.2337\/diacare.27.5.1047","journal-title":"Diabetes Care"},{"issue":"9","key":"12642_CR140","doi-asserted-by":"publisher","first-page":"1677","DOI":"10.1016\/S0161-6420(03)00475-5","volume":"110","author":"CP Wilkinson","year":"2003","unstructured":"Wilkinson CP, Ferris FL, Klein RE, Lee PP, Agardh CD, Davis M, Dills D, Kampik A, Pararajasegaram R, Verdaguer JT (2003) Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales. Global diabetic retinopathy project group. Ophthalmology. 110(9):1677\u20131682. https:\/\/doi.org\/10.1016\/S0161-6420(03)00475-5","journal-title":"Ophthalmology."},{"key":"12642_CR141","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.compmedimag.2016.08.001","volume":"55","author":"B Wu","year":"2017","unstructured":"Wu B, Zhu W, Shi F, Zhu S, Chen X (2017) Automatic detection of microaneurysms in retinal fundus images. Comput Med Imaging Graph 55:106\u2013112. https:\/\/doi.org\/10.1016\/j.compmedimag.2016.08.001","journal-title":"Comput Med Imaging Graph"},{"issue":"12","key":"12642_CR142","doi-asserted-by":"publisher","first-page":"2054","DOI":"10.3390\/molecules22122054","volume":"22","author":"K Xu","year":"2017","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):2054. https:\/\/doi.org\/10.3390\/molecules22122054","journal-title":"Molecules"},{"key":"12642_CR143","doi-asserted-by":"publisher","unstructured":"Yu F, Sun J, Li A, Cheng J, Wan C, Liu J (2017) Image quality classification for DR screening using deep learning. 2017 39th annual international conference of the IEEE engineering in medicine and biology society. 664-667. https:\/\/doi.org\/10.1109\/EMBC.2017.8036912","DOI":"10.1109\/EMBC.2017.8036912"},{"issue":"6","key":"12642_CR144","doi-asserted-by":"publisher","first-page":"2237","DOI":"10.1016\/j.patcog.2009.12.017","volume":"43","author":"B Zhang","year":"2010","unstructured":"Zhang B, Wu X, You J, Li Q, Karray F (2010) Detection of microaneurysms using multi-scale correlation coefficients. Pattern Recogn 43(6):2237\u20132248. https:\/\/doi.org\/10.1016\/j.patcog.2009.12.017","journal-title":"Pattern Recogn"},{"key":"12642_CR145","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.ins.2012.03.003","volume":"200","author":"B Zhang","year":"2012","unstructured":"Zhang B, Karray F, Li Q, Zhang L (2012) Sparse representation classifier for microaneurysm detection and retinal blood vessel extraction. Inf Sci 200:78\u201390. https:\/\/doi.org\/10.1016\/j.ins.2012.03.003","journal-title":"Inf Sci"},{"key":"12642_CR146","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2019\/8973287","volume":"2019","author":"W Zhou","year":"2019","unstructured":"Zhou W, Yi Y, Gao Y, Dai J (2019) Optic disc and cup segmentation in retinal images for Glaucoma diagnosis by locally statistical active contour model with structure prior. Comput Math Methods Med 2019:1\u201317. https:\/\/doi.org\/10.1155\/2019\/8973287","journal-title":"Comput Math Methods Med"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-12642-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-022-12642-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-12642-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T12:00:30Z","timestamp":1744200030000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-022-12642-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,23]]},"references-count":146,"journal-issue":{"issue":"18","published-print":{"date-parts":[[2022,7]]}},"alternative-id":["12642"],"URL":"https:\/\/doi.org\/10.1007\/s11042-022-12642-4","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,23]]},"assertion":[{"value":"25 November 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 June 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 February 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 March 2022","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 have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}