A Survey on Intelligent Screening for Diabetic Retinopathy

Dai Yulan; Zhu Chengzhang; Shan Xi; Cheng Zhenzhen; Zou Beiji

doi:10.24920/003587

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A Survey on Intelligent Screening for Diabetic Retinopathy

Chinese Medical Sciences Journal;Chinese Medical Sciences Journal Vol. 34, Issue 2, Pages: 120-132(2019)

Review | Updated：2024-04-10

- A Survey on Intelligent Screening for Diabetic Retinopathy
- Affiliations：
  
  1. School of Computing Science and Engineering
  2. School of Literature and Journalism, Central South University, Changsha 410083, China
  3. Hunan Province Engineering Technology Research Center of Computer Vision and Intelligent Medical Treatment, Changsha 410083, China
- Author bio：
  
  *, E-mail: anandawork@126.com
- Funds：
- DOI：10.24920/003587
  CLC：
- Received：22 March 2019，
  
  Accepted：2019-5-6，
  
  Published Online：20 May 2019，
  
  Published：20 May 2019
- Accepted：
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Dai Yulan, Zhu Chengzhang, Shan Xi, et al. A Survey on Intelligent Screening for Diabetic Retinopathy[J]. Chinese medical sciences journal;chinese medical sciences journal, 2019, 34(2): 120-132.
DOI：

Dai Yulan, Zhu Chengzhang, Shan Xi, et al. A Survey on Intelligent Screening for Diabetic Retinopathy[J]. Chinese medical sciences journal;chinese medical sciences journal, 2019, 34(2): 120-132. DOI： 10.24920/003587.

摘要

糖尿病视网膜病变是导致失明的主要原因之一

但通过筛查、早期诊断和治疗可以有效避免该并发症的发生。为了提高糖尿病视网膜病变筛查的广泛性和效率

研究者们在智能筛查方面投入了大量的精力

并取得了较大的进展。本文从四个方面对糖尿病视网膜病变的筛查进行了系统地综述: 1) 公共彩色眼底图像数据集;2) 分类及相关病变提取方法;3)现有的计算机辅助筛查系统;4)存在的问题、挑战和研究趋势。希望此文能为糖尿病视网膜病变智能筛查的未来研究方向提供参考。

Abstract

Diabetic retinopathy (DR) is one of the leading causes of vision loss and can be effectively avoided by screening

early diagnosis and treatment. In order to increase the universality and efficiency of DR screening

many efforts have been invested in developing intelligent screening

and there have been great advances. In this paper

we survey DR screening from four perspectives: 1) public color fundus image datasets of DR; 2) DR classification and related lesion-extraction approaches; 3) existing computer-aided systems for DR screening; and 4) existing issues

challenges

and research trends. Our goal is to provide insights for future research directions on DR intelligent screening.

关键词

Keywords

references

Yau JWY , Rogers SL , Kawasaki R , et al . Global preva-lence and major risk factors of diabetic retinopathy . Diabetes Care 2012 ; 35 ( 3 ): 556 - 64 .doi: DOI: 10.2337/dc11-1909 http://doi.org/10.2337/dc11-1909 http://care.diabetesjournals.org/cgi/doi/10.2337/dc11-1909 http://care.diabetesjournals.org/cgi/doi/10.2337/dc11-1909

Xu Y , Wang L , He J , et al . Prevalence and control of diabetes in Chinese adults . JAMA 2013 ; 310 ( 9 ): 948 - 59 . doi: DOI: 10.1001/jama.2013.168118 http://doi.org/10.1001/jama.2013.168118 http://jama.jamanetwork.com/article.aspx?doi=10.1001/jama.2013.168118 http://jama.jamanetwork.com/article.aspx?doi=10.1001/jama.2013.168118

Zhao KX , Yang PZ , Zhai J , et al. Retinopathy. In: Zhao KX, Yang PZ, editors. Ophthalmology, 8th ed . Beijing: People’s Medical Publishing House ; 2013 . p. 217 - 9 .

Kohner EM , Stratton IM , Aldington SJ , et al . Micro-aneurysms in the development of diabetic retinopathy (UKPDS 42) . Diabetologia 1999 ; 42 ( 9 ): 1107 - 12 . doi: DOI: 10.1007/s001250051278 http://doi.org/10.1007/s001250051278 DOI: 10.1007/s001250051278 http://doi.org/10.1007/s001250051278 http://link.springer.com/10.1007/s001250051278 http://link.springer.com/10.1007/s001250051278

Teng T , Lefley M , Claremont D . Progress towards automated diabetic ocular screening: A review of image analysis and intelligent systems for diabetic retinopathy . Med Biol Eng Comput 2002 ; 40 ( 1 ): 2 - 13 . doi: DOI: 10.1007/BF02347689 http://doi.org/10.1007/BF02347689 http://link.springer.com/10.1007/BF02347689 http://link.springer.com/10.1007/BF02347689

Hatanaka Y , Nakagawa T , Hayashi Y , et al . CAD scheme to detect hemorrhages and exudates in ocular fundus images. Proceedings of the SPIE of Medical Imaging. 2007 Mar 30; San Diego , USA. 2007 . doi: DOI: 10.1117/12.708367 http://doi.org/10.1117/12.708367 .

Agurto C , Murray V , Barriga E , et al . Multiscale AM-FM methods for diabetic retinopathy lesion detection . IEEE Trans Med Imaging 2010 ; 29 ( 2 ): 502 - 12 . doi: DOI: 10.1109/TMI.2009.2037146 http://doi.org/10.1109/TMI.2009.2037146 http://ieeexplore.ieee.org/document/5405648/ http://ieeexplore.ieee.org/document/5405648/

Ravishankar S , Jain A , Mittal A . Automated feature extraction for early detection of diabetic retinopathy in fundus images. Proceedings of the 22nd IEEE Conference on Computer Vision and Pattern Recognition . 2009 Jun 20-25; Miami, USA. Los Alamitos : IEEE Computer Society Press ; 2009 . p. 210 - 7 . doi: DOI: 10.1109/CVPR.2009.5206763 http://doi.org/10.1109/CVPR.2009.5206763 .

Tǎlu S , Cǎlugǎru DM , Lupascu CA . Characterisation of human non-proliferative diabetic retinopathy using the fractal analysis . Int J ophthalmol 2015 ; 8 ( 4 ): 770 - 6 . doi: DOI: 10.3980/j.issn.2222-3959.2015.04.23 http://doi.org/10.3980/j.issn.2222-3959.2015.04.23 .

Haloi M . Improved microaneurysm detection using deep neural networks . Comput Sci 2015 ; arXiv: 1505 . 04424 .

Prentašić P , Lončarić S . Detection of exudates in fundus photographs using convolutional neural networks. Proceedings of the 9th IEEE International Symposium on Image and Signal Processing and Analysis . 2015 Sep 7-9; Zagreb, Croatia. Los Alamitos : IEEE Computer Society Press ; 2015 . p. 188 - 92 . doi: DOI: 10.1109/ISPA.2015.7306056 http://doi.org/10.1109/ISPA.2015.7306056 .

Tan J H , Fujita H , Sivaprasad S , et al . Automated segmentation of exudates, haemorrhages, microaneurysms using single convolutional neural network . Inf Sci 2017 ; ( 420 ): 66 - 76 . doi: DOI: 10.1016/j.ins.2017.08.050 http://doi.org/10.1016/j.ins.2017.08.050 .

Kauppi T , Kalesnykiene V , Kamarainen J , et al . DIARETDB0: evaluation database and methodology for diabetic retinopathy algorithms. 2006 .

Kauppi T , Kalesnykiene V , Kamarainen JK , et al . DIARETDB1 diabetic retinopathy database and evaluation protocol. Proceedings of the 11st British Machine Vision Conference . 2007 Sep 10-13; Warwick, UK. 2007 . p. 61 - 5 . doi: DOI: 10.5244/C.21.15 http://doi.org/10.5244/C.21.15 .

Decencière E , Zhang XW , Cazuguel G , et al . Feedback on a publicly distributed image database: The messidor database . Image Anal Stereol 2014 ; 33 ( 3 ): 231 - 4 . doi: DOI: 10.5566/ias.1155 http://doi.org/10.5566/ias.1155 https://www.ias-iss.org/ojs/IAS/article/view/1155 https://www.ias-iss.org/ojs/IAS/article/view/1155

Hoover A , Kouznetsova V , Goldbaum M , et al . Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response . IEEE Trans Med Imaging 2000 ; 19 ( 3 ): 203 - 10 . doi: DOI: 10.1109/42.845178 http://doi.org/10.1109/42.845178 http://ieeexplore.ieee.org/document/845178/ http://ieeexplore.ieee.org/document/845178/

Odstrcilik J , Kolar R , Budai A , et al . Retinal vessel segmentation by improved matched filtering: evaluation on a new high-resolution fundus image database . IET Image Processing 2013 ; 7 ( 4 ): 373 - 83 . doi: DOI: 10.1049/iet-ipr.2012.0455 http://doi.org/10.1049/iet-ipr.2012.0455 https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2012.0455 https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2012.0455

Prasanna P , Samiksha P , Ravi K , et al . Indian Diabetic Retinopathy Image Dataset (IDRiD): a database for diabetic retinopathy screening research . Data 2018 ; 3 ( 3 ): 25 - 32 . doi: DOI: 10.3390/data3030025 http://doi.org/10.3390/data3030025 http://www.mdpi.com/2306-5729/3/3/25 http://www.mdpi.com/2306-5729/3/3/25

Kaggle Inc . Diabetic Retinopathy Detection . . Published 2015 ; accessed February 28 , 2019 .https://www.kaggle.com/c/diabetic-retinopathy-detection/data https://www.kaggle.com/c/diabetic-retinopathy-detection/data https://www.kaggle.com/c/diabetic-retinopathy-detection/data

Decencière E , Cazuguel G , Zhang X , et al . TeleOphta: machine learning and image processing methods for teleophthalmology . IRBM 2013 ; 34 ( 2 ): 196 - 203 . doi: DOI: 10.1016/j.irbm.2013.01.010 http://doi.org/10.1016/j.irbm.2013.01.010 https://linkinghub.elsevier.com/retrieve/pii/S1959031813000237 https://linkinghub.elsevier.com/retrieve/pii/S1959031813000237

Niemeijer M , Ginneken BV , Cree MJ , et al . Retinopathy online challenge: automatic detection of microaneurysms in digital color fundus photographs . IEEE Trans Med Imaging 2009 ; 29 ( 1 ): 185 - 95 . doi: DOI: 10.1109/TMI.2009.2033909 http://doi.org/10.1109/TMI.2009.2033909

Wong Y , Lam B , Lee J , et al . Development of an automatic retinal image analysis (ARIA) for screening cerebral small vessel disease in the community . J Neurol Sci 2017 ; 381 : 959 . doi: DOI: 10.1016/j.jns.2017.08.2700 http://doi.org/10.1016/j.jns.2017.08.2700

Prentasic P , Loncaric S , Vatavuk Z , et al. Diabetic reti-nopathy image database (DRiDB): a new database for diabetic retinopathy screening programs research. Proceedings of the 8th International Symposium on Image and Signal Processing and Analysis. 2013 Sep 4-6; Trieste, Italy. Los Alamitos : IEEE Computer Society Press ; 2013 . p. 712 - 6 . doi: DOI: 10.1109/ISPA.2013.6703830 http://doi.org/10.1109/ISPA.2013.6703830 .

Winder RJ , Morrow PJ , Mcritchie IN , et al . Algorithms for digital image processing in diabetic retinopathy . Comput Med Imaging Graphics 2009 ; 33 ( 8 ): 608 - 22 . doi: DOI: 10.1016/j.compmedimag.2009.06.003 http://doi.org/10.1016/j.compmedimag.2009.06.003 https://linkinghub.elsevier.com/retrieve/pii/S0895611109000810 https://linkinghub.elsevier.com/retrieve/pii/S0895611109000810

Lay B , Baudoin C , Klein JC . Automatic detection of microaneurysms in retinopathy fluoro-angiogram. Proceedings of SPIE-the International Society for Optical Engineering. 1984 ; 432 : 165 - 73 . doi: DOI: 10.1117/12.936655 http://doi.org/10.1117/12.936655

Spencer T , Olson JA , Mchardy KC , et al . An image-processing strategy for the segmentation and quantification of microaneurysms in fluorescein angiograms of the ocular fundus . Comput Biomed Res Int J 1996 ; 29 ( 4 ): 284 - 302 . doi: DOI: 10.1006/cbmr.1996.0021 http://doi.org/10.1006/cbmr.1996.0021 https://linkinghub.elsevier.com/retrieve/pii/S001048099690021X https://linkinghub.elsevier.com/retrieve/pii/S001048099690021X

Hipwell JH , Strachan F , Olson JA , et al . Automated detection of microaneurysms in digital red‐free photographs: a diabetic retinopathy screening tool . Diabetic Med 2001 ; 17 ( 8 ): 588 - 94 . doi: DOI: 10.1046/j.1464-5491.2000.00338.x http://doi.org/10.1046/j.1464-5491.2000.00338.x

Fleming AD , Philip S , Goatman KA , et al . Automated microaneurysm detection using local contrast normalization and local vessel detection . IEEE Trans Med Imaging 2006 ; 25 ( 9 ): 1223 - 32 . doi: DOI: 10.1109/TMI.2006.879953 http://doi.org/10.1109/TMI.2006.879953 http://ieeexplore.ieee.org/document/1677728/ http://ieeexplore.ieee.org/document/1677728/

Puranik SS , Malode VB . Morphology based approach for microaneurysm detection from retinal image. 2016 Processing of the International Conference on Automatic Control and Dynamic Optimization Techniques. 2016 Sep 9-10; Pune, India. Los Alamitos : IEEE Computer Society Press ; 2016 . p. 635 - 9 . doi: DOI: 10.1109/ICACDOT.2016.7877663 http://doi.org/10.1109/ICACDOT.2016.7877663 .

Kamble R , Kokare M . Detection of microaneurysm using local rank transform in color fundus images. ICIP 2017 : Processing of the 24th IEEE International Conference on Image Processing. 2017 Sep 17-20; Beijing, China. Los Alamitos: IEEE Computer Society Press ; 2017 . p. 4442 - 6 . doi: DOI: 10.1109/ICIP.2017.8297122 http://doi.org/10.1109/ICIP.2017.8297122 .

Gwénolé Q , Lamard M , Josselin PM , et al . Optimal wavelet transform for the detection of microaneurysms in retina photographs . IEEE Trans Med Imaging 2008 ; 27 ( 9 ): 1230 - 41 . doi: DOI: 10.1109/TMI.2008.920619 http://doi.org/10.1109/TMI.2008.920619 http://ieeexplore.ieee.org/document/4547597/ http://ieeexplore.ieee.org/document/4547597/

Zhang B , Wu X , You J , et al . Detection of microaneurysms using multi-scale correlation coefficients . Pattern Recognition 2010 ; 43 ( 6 ): 2237 - 48 . doi: DOI: 10.1016/j.patcog.2009.12.017 http://doi.org/10.1016/j.patcog.2009.12.017 https://linkinghub.elsevier.com/retrieve/pii/S003132031000004X https://linkinghub.elsevier.com/retrieve/pii/S003132031000004X

Antal B , Hajdu A . An ensemble-based system for microaneurysm detection and diabetic retinopathy grading . IEEE Trans Biomed Eng 2012 ; 59 ( 6 ): 1720 - 6 . doi: DOI: 10.1109/TBME.2012.2193126 http://doi.org/10.1109/TBME.2012.2193126 http://ieeexplore.ieee.org/document/6177649/ http://ieeexplore.ieee.org/document/6177649/

Michael A , Ginneken B V , Meindert N , et al . Automatic detection of red lesions in digital color fundus photographs . IEEE Trans Med Imaging 2009 ; 24 ( 5 ): 584 - 92 . doi: DOI: 10.1109/TMI.2005.843738 http://doi.org/10.1109/TMI.2005.843738

Dashtbozorg B , Zhang J , Huang F , et al . Retinal microaneurysms detection using local convergence index features . IEEE Trans Image Processing 2018 ; 27 ( 7 ): 3300 - 15 . doi: DOI: 10.1109/TIP.2018.2815345 http://doi.org/10.1109/TIP.2018.2815345 https://ieeexplore.ieee.org/document/8314699/ https://ieeexplore.ieee.org/document/8314699/

Wang S , Tang HL , Turk LIA , et al . Localizing microaneurysms in fundus images through singular spectrum analysis . IEEE Trans Biomed Eng 2016 ; 64 ( 5 ): 990 - 1002 . doi: DOI: 10.1109/TBME.2016.2585344 http://doi.org/10.1109/TBME.2016.2585344 .

Xu J , Zhang X , Chen H , et al . Automatic analysis of microaneurysms turn over to diagnose the progression of diabetic retinopathy . IEEE Access 2018 ; 6 : 9632 - 42 . doi: DOI: 10.1109/ACCESS.2018.2808160 http://doi.org/10.1109/ACCESS.2018.2808160 http://ieeexplore.ieee.org/document/8295214/ http://ieeexplore.ieee.org/document/8295214/

Chudzik P , Majumdar S , Calivá F , et al . Microaneurysm detection using fully convolutional neural networks . Comput Methods Programs Biomed 2018 ; 158 : 185 - 92 . doi: DOI: 10.1016/j.cmpb.2018.02.016 http://doi.org/10.1016/j.cmpb.2018.02.016 https://linkinghub.elsevier.com/retrieve/pii/S0169260717308544 https://linkinghub.elsevier.com/retrieve/pii/S0169260717308544

Budak U , Şengür A , Guo Y , et al . A novel microaneurysms detection approach based on convolutional neural networks with reinforcement sample learning algorithm . Health Inf Sci Syst 2017 ; 5 ( 1 ): 14 . doi: DOI: 10.1007/s13755-017-0034-9 http://doi.org/10.1007/s13755-017-0034-9 http://link.springer.com/10.1007/s13755-017-0034-9 http://link.springer.com/10.1007/s13755-017-0034-9

Dai L , Fang R , Li H , et al . Clinical report guided retinal microaneurysm detection with multi-sieving deep learning . IEEE Trans Med Imaging 2018 : 1149 - 61 . doi: DOI: 10.1109/TMI.2018.2794988 http://doi.org/10.1109/TMI.2018.2794988 .

Bae JP , Kim KG , Kang HC , et al . A study on hemorrhage detection using hybrid method in fundus images . J Digital Imaging 2011 ; 24 ( 3 ): 394 - 404 . doi: DOI: 10.1007/s10278-010-9274-9 http://doi.org/10.1007/s10278-010-9274-9 http://link.springer.com/10.1007/s10278-010-9274-9 http://link.springer.com/10.1007/s10278-010-9274-9

Xiao D , Yu S , Vignarajan J , et al. Retinal hemorrhage detection by rule-based and machine learning approach. 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2017 Jul 11-15; Seogwipo, South Korea. Los Alamitos : IEEE Computer Society Press ; 2017 . p. 660 - 3 . doi: DOI: 10.1109/EMBC.2017.8036911 http://doi.org/10.1109/EMBC.2017.8036911 .

Tang L , Niemeijer M , Reinhardt JM , et al . Splat feature classification with application to retinal hemorrhage detection in fundus images . IEEE Trans Med Imaging 2013 ; 32 ( 2 ): 364 - 75 . doi: DOI: 10.1109/TMI.2012.2227119 http://doi.org/10.1109/TMI.2012.2227119 http://ieeexplore.ieee.org/document/6352921/ http://ieeexplore.ieee.org/document/6352921/

Inbarathi R , Karthikeyan R . Detection of retinal hemo-rrhage in fundus images by classifying the splat features using SVM . Int J Innovat Res Sci Eng Technol 2014 ; 3 ( 3 ): 1979 - 86 .

Van Grinsven M , Van Ginneken B , Hoyng C , et al . 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 - 84 . doi: DOI: 10.1109/TMI.2016.2526689 http://doi.org/10.1109/TMI.2016.2526689 http://ieeexplore.ieee.org/document/7401052/ http://ieeexplore.ieee.org/document/7401052/

Kande GB , Savithri TS , Subbaiah PV . Automatic detection of microaneurysms and hemorrhages in digital fundus images . J Digital Imaging 2010 ; 23 ( 4 ): 430 - 7 . doi: DOI: 10.1007/s10278-009-9246-0 http://doi.org/10.1007/s10278-009-9246-0 http://link.springer.com/10.1007/s10278-009-9246-0 http://link.springer.com/10.1007/s10278-009-9246-0

Akram UM , Khan SA . Automated detection of dark and bright lesions in retinal images for early detection of diabetic retinopathy . J Med Syst 2012 ; 36 ( 5 ): 3151 - 62 . doi: DOI: 10.1007/s10916-011-9802-2 http://doi.org/10.1007/s10916-011-9802-2 http://link.springer.com/10.1007/s10916-011-9802-2 http://link.springer.com/10.1007/s10916-011-9802-2

Michael A , Ginneken BV , Meindert N , et al . Automatic detection of red lesions in digital color fundus photographs . IEEE Trans Med Imaging 2005 ; 24 ( 5 ): 584 - 92 . doi: DOI: 10.1109/TMI.2005.843738 http://doi.org/10.1109/TMI.2005.843738 http://ieeexplore.ieee.org/document/1425665/ http://ieeexplore.ieee.org/document/1425665/

Seoud L , Hurtut T , Chelbi J , et al . Red lesion detection using dynamic shape features for diabetic retinopathy screening . IEEE Trans Med Imaging 2016 ; 35 ( 4 ): 1116 - 26 . doi: DOI: 10.1109/TMI.2015.2509785 http://doi.org/10.1109/TMI.2015.2509785 http://ieeexplore.ieee.org/document/7360182/ http://ieeexplore.ieee.org/document/7360182/

Adal KM , Van Etten PG , Martinez JP , et al . An automated system for the detection and classification of retinal changes due to red lesions in longitudinal fundus images . IEEE Trans Biomed Eng 2017 ; 65 ( 6 ): 1382 - 90 . doi: DOI: 10.1109/TBME.2017.2752701 http://doi.org/10.1109/TBME.2017.2752701 .

Walter T , Klein JC , Massin P , et al . A contribution of image processing to the diagnosis of diabetic retino-pathy-Detection of exudates in color fundus images of the human retina . Trans Med Imaging 2002 ; 21 ( 10 ): 1236 . doi: DOI: 10.1109/TMI.2002.806290 http://doi.org/10.1109/TMI.2002.806290 http://ieeexplore.ieee.org/document/1174101/ http://ieeexplore.ieee.org/document/1174101/

Figueiredo IN , Kumar S , Oliveira CM , et al . Automated lesion detectors in retinal fundus images . Comput Biol Med 2015 ; 66 ( C ): 47 - 65 . doi: DOI: 10.1016/j.compbiomed.2015.08.008 http://doi.org/10.1016/j.compbiomed.2015.08.008 https://linkinghub.elsevier.com/retrieve/pii/S0010482515002851 https://linkinghub.elsevier.com/retrieve/pii/S0010482515002851

Sopharak A , Uyyanonvara B , Barman S , et al . Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods . Comput Med Imaging Graph 2008 ; 32 ( 8 ): 720 - 7 . doi: DOI: 10.1016/j.compmedimag.2008.08.009 http://doi.org/10.1016/j.compmedimag.2008.08.009 https://linkinghub.elsevier.com/retrieve/pii/S0895611108000931 https://linkinghub.elsevier.com/retrieve/pii/S0895611108000931

Kar SS , Maity S . Automatic detection of retinal lesions for screening of diabetic retinopathy . IEEE Trans Biomed Eng 2018 ; 65 ( 3 ): 608 - 18 . doi: DOI: 10.1109/TBME.2017.2707578 http://doi.org/10.1109/TBME.2017.2707578 http://ieeexplore.ieee.org/document/7933211/ http://ieeexplore.ieee.org/document/7933211/

Sánchez CI , Hornero R , López MI , et al . A novel automatic image processing algorithm for detection of hard exudates based on retinal image analysis . Med Eng Physics 2008 ; 30 ( 3 ): 350 - 7 . doi: DOI: 10.1016/j.medengphy.2007.04.010 http://doi.org/10.1016/j.medengphy.2007.04.010 https://linkinghub.elsevier.com/retrieve/pii/S1350453307000677 https://linkinghub.elsevier.com/retrieve/pii/S1350453307000677

Li H , Chutatape O . Automated feature extraction in color retinal images by a model based approach . IEEE Trans Biomed Eng 2004 ; 51 ( 2 ): 246 - 54 . doi: DOI: 10.1109/TBME.2003.820400 http://doi.org/10.1109/TBME.2003.820400 http://ieeexplore.ieee.org/document/1262102/ http://ieeexplore.ieee.org/document/1262102/

Wisaeng K , Sa-Ngiamvibool W . Exudates detection using morphology mean shift algorithm in retinal images . IEEE Access 2019 ; 7 : 11946 - 58 . doi: 10.1109/ACCESS.2018.2890426 1001-9294-34-2-120/ACCESS.2018.2890426 . DOI: 10.1109/ACCESS.2018.2890426 http://doi.org/10.1109/ACCESS.2018.2890426 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639

Youssef D , Solouma NH . Accurate detection of blood vessels improves the detection of exudates in color fundus images . Comput Methods Programs Biomed 2012 ; 108 ( 3 ): 1052 - 61 . doi: DOI: 10.1016/j.cmpb.2012.06.006 http://doi.org/10.1016/j.cmpb.2012.06.006 https://linkinghub.elsevier.com/retrieve/pii/S0169260712001605 https://linkinghub.elsevier.com/retrieve/pii/S0169260712001605

Osareh A , Mirmehdi M , Thomas B , et al. Comparative exudate classification using support vector machines and neural networks. in: Dohi T, Kikinis R, editors. Medical Image Computing and Computer Assisted Intervention-MICCAI 2002 . Berlin : Springer ; 2002 . p. 413 - 20 . doi: DOI: 10.1007/3-540-45787-9_52 http://doi.org/10.1007/3-540-45787-9_52 .

Osareh A , Shadgar B , Markham R . A computational intelligence-based approach for detection of exudates in diabetic retinopathy images . IEEE Trans Inf Technol Biomed 2009 ; 13 ( 4 ): 535 - 45 . doi: DOI: 10.1109/TITB.2008.2007493 http://doi.org/10.1109/TITB.2008.2007493 http://ieeexplore.ieee.org/document/5156192/ http://ieeexplore.ieee.org/document/5156192/

Zhang XW , Thibault G , Decencière E , et al . Exudate detection in color retinal images for mass screening of diabetic retinopathy . Med Image Anal 2014 ; 18 ( 7 ): 1026 - 43 . doi: DOI: 10.1016/j.media.2014.05.004 http://doi.org/10.1016/j.media.2014.05.004 https://linkinghub.elsevier.com/retrieve/pii/S1361841514000693 https://linkinghub.elsevier.com/retrieve/pii/S1361841514000693

Harangi B , Antal B , Hajdu A . Automatic exudate detection with improved Naïve-bayes classifier. 2012 25th IEEE International Symposium on Computer-Based Medical Systems(CBMS). 2012 Jun 20-22; Rome, Italy. Los Alamitos : IEEE Computer Society Press ; 2012 . p. 139 - 142 . doi: DOI: 10.1109/CBMS.2012.6266341 http://doi.org/10.1109/CBMS.2012.6266341 .

García M , Sánchez CI , López MI , et al . Neural network based detection of hard exudates in retinal images . Comput Methods Programs Biomed 2009 ; 93 ( 1 ): 9 - 19 . doi: DOI: 10.1016/j.cmpb.2008.07.006 http://doi.org/10.1016/j.cmpb.2008.07.006 https://linkinghub.elsevier.com/retrieve/pii/S0169260708001855 https://linkinghub.elsevier.com/retrieve/pii/S0169260708001855

Yu S , Xiao D , Kanagasingam Y . Exudate detection for diabetic retinopathy with convolutional neural networks. 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (CMBC). 2017 Jul 11-15; Seogwipo, South Korea. Los Alamitos : IEEE Computer Society Press ; 2017 . p. 1744 - 7 . doi: DOI: 10.1109/EMBC.2017.8037180 http://doi.org/10.1109/EMBC.2017.8037180 .

Prentašić P , Lončarić S . Detection of exudates in fundus photographs using deep neural networks and anatomical landmark detection fusion . Comput Methods Programs Biomed 2016 ; 137 : 281 - 92 . doi: DOI: 10.1016/j.cmpb.2016.09.018 http://doi.org/10.1016/j.cmpb.2016.09.018 https://linkinghub.elsevier.com/retrieve/pii/S0169260716308033 https://linkinghub.elsevier.com/retrieve/pii/S0169260716308033

Herbert FJ , Michael JC , Jorge JGL , et al . Automated segmentation of retinal blood vessels and identification of proliferative diabetic retinopathy . J Opt Soc Am A 2007 ; 24 ( 5 ): 1448 - 56 . doi: DOI: 10.1364/JOSAA.24.001448 http://doi.org/10.1364/JOSAA.24.001448 https://www.osapublishing.org/abstract.cfm?URI=josaa-24-5-1448 https://www.osapublishing.org/abstract.cfm?URI=josaa-24-5-1448

Welikala RA , Dehmeshki J , Hoppe A , et al . Automated detection of proliferative diabetic retinopathy using a modified line operator and dual classification . Comput Methods Programs Biomed 2014 ; 114 ( 3 ): 247 - 61 . doi: DOI: 10.1016/j.cmpb.2014.02.010 http://doi.org/10.1016/j.cmpb.2014.02.010 https://linkinghub.elsevier.com/retrieve/pii/S0169260714000650 https://linkinghub.elsevier.com/retrieve/pii/S0169260714000650

Lee J , Zee B C , Li Q . Detection of neovascularization based on fractal and texture analysis with interaction effects in diabetic retinopathy . Plos One 2013 ; 8 ( 12 ): e75699 . doi: DOI: 10.1371/journal.pone.0075699 http://doi.org/10.1371/journal.pone.0075699 https://dx.plos.org/10.1371/journal.pone.0075699 https://dx.plos.org/10.1371/journal.pone.0075699

Yu S , Xiao D , Kanagasingam Y . Machine learning based automatic neovascularization detection on optic disc region . IEEE J Biomed Health Inform 2018 ; 22 ( 3 ): 886 - 94 . doi: DOI: 10.1109/JBHI.2017.2710201 http://doi.org/10.1109/JBHI.2017.2710201 https://ieeexplore.ieee.org/document/7936460/ https://ieeexplore.ieee.org/document/7936460/

Roychowdhury S , Koozekanani DD , Parhi KK , et al. Automated detection of neovascularization for proliferative diabetic retinopathy screening. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2016 Aug 16-20; Orlando, USA. Los Alamitos : IEEE Computer Society Press ; 2016 . p. 1300 - 3 . doi: DOI: 10.1109/EMBC.2016.7590945 http://doi.org/10.1109/EMBC.2016.7590945 .

Goatman KA , Fleming AD , Philip S , et al . Detection of new vessels on the optic disc using retinal photographs . IEEE Trans Med Imaging 2011 ; 30 ( 4 ): 972 - 9 . doi: DOI: 10.1109/TMI.2010.2099236 http://doi.org/10.1109/TMI.2010.2099236 http://ieeexplore.ieee.org/document/5667059/ http://ieeexplore.ieee.org/document/5667059/

Nayak J , Bhat PS , Rajendra AU , et al . Automated identification of diabetic retinopathy stages using digital fundus images . J Med Syst 2008 ; 32 ( 2 ): 107 - 15 . doi: DOI: 10.1007/s10916-007-9113-9 http://doi.org/10.1007/s10916-007-9113-9 http://link.springer.com/10.1007/s10916-007-9113-9 http://link.springer.com/10.1007/s10916-007-9113-9

Frame AJ , Undrill PE , Olson JA , et al. Texture analysis of retinal neovascularization. Proceedings of the 7th International Conference on Pattern Recognition. 1997 Feb 26-26; London, UK. Los Alamitos : IEEE Computer Society Press ; 1997 . p. 511 - 6 . doi: DOI: 10.1049/ic:19970128 http://doi.org/10.1049/ic:19970128 .

Pujitha AK , Jahnavi GS , Sivaswamy J . Detection of neovascularization in retinal images using semi-supervised learning. 2017 14th IEEE International Symposium on Biomedical Imaging (ISBI). 2017 Apr 18-21; Melbourne, Australia. Los Alamitos : IEEE Computer Society Press ; 2017 . p. 688 - 91 . doi: DOI: 10.1109/ISBI.2017.7950613 http://doi.org/10.1109/ISBI.2017.7950613 .

Gupta G , Kulasekaran S , Ram K , et al . Local characterization of neovascularization and identification of proliferative diabetic retinopathy in retinal fundus images . Comput Med Imaging Graphics 2017 ; 124 - 32 . doi: DOI: 10.1016/j.compmedimag.2016.08.005 http://doi.org/10.1016/j.compmedimag.2016.08.005

Morales S , Engan K , Naranjo V , et al . Retinal disease screening through local binary patterns . IEEE J Biomed Health Inform 2017 ; 21 ( 1 ): 184 - 92 . doi: DOI: 10.1109/JBHI.2015.2490798 http://doi.org/10.1109/JBHI.2015.2490798 http://ieeexplore.ieee.org/document/7298393/ http://ieeexplore.ieee.org/document/7298393/

Mookiah MRK , Acharya UR , Martis RJ , et al . Evolutionary algorithm based classifier parameter tuning for automatic diabetic retinopathy grading: A hybrid feature extraction approach . Knowledge-Based Syst 2013 ; 39 : 9 - 22 . doi: DOI: 10.1016/j.knosys.2012.09.008 http://doi.org/10.1016/j.knosys.2012.09.008 https://linkinghub.elsevier.com/retrieve/pii/S0950705112002651 https://linkinghub.elsevier.com/retrieve/pii/S0950705112002651

Rubini SS , Kunthavai A . Diabetic retinopathy detection based on eigenvalues of the hessian matrix . Procedia Comput Sci 2015 ; 47 : 311 - 8 . doi: DOI: 10.1016/j.procs.2015.04.001 http://doi.org/10.1016/j.procs.2015.04.001 https://linkinghub.elsevier.com/retrieve/pii/S1877050915005025 https://linkinghub.elsevier.com/retrieve/pii/S1877050915005025

Bhatkar AP , Kharat GU . Detection of diabetic retinopathy in retinal images using MLP classifier. 2015 15th IEEE International Symposium on Nanoelectronic and Information Systems. 2015 Dec 21-23; Indore, India. Los Alamitos : IEEE Computer Society Press ; 2015 . p. 331 - 5 . doi: DOI: 10.1109/iNIS.2015.30 http://doi.org/10.1109/iNIS.2015.30 .

Gardner G , Keating D , Williamson T , et al . Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool . Br J Ophthalmol 1996 ; 80 ( 11 ): 940 - 4 . doi: DOI: 10.1136/bjo.80.11.940 http://doi.org/10.1136/bjo.80.11.940 http://bjo.bmj.com/cgi/doi/10.1136/bjo.80.11.940 http://bjo.bmj.com/cgi/doi/10.1136/bjo.80.11.940

Usher D , Dumskyj M , Himaga M , et al . Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening . Diabetic Med 2004 ; 21 ( 1 ): 84 - 90 . doi: DOI: 10.1046/j.1464-5491.2003.01085.x http://doi.org/10.1046/j.1464-5491.2003.01085.x http://doi.wiley.com/10.1046/j.1464-5491.2003.01085.x http://doi.wiley.com/10.1046/j.1464-5491.2003.01085.x

Szegedy C , Liu W , Jia Y , et al. Going deeper with convolutions. 2015 CVPR: Proceedings of the 28th IEEE Conference Computer Vision and Pattern Recognition. 2015 Jun 7-12; Boston, USA . Los Alamitos : IEEE Computer Society Press ; 2015 . p. 1 - 9 . doi: DOI: 10.1109/CVPR.2015.7298594 http://doi.org/10.1109/CVPR.2015.7298594 .

Gulshan V , Peng L , Coram M , et al . Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs . J Am Med Assoc 2016 ; 316 ( 22 ): 2402 - 10 . doi: DOI: 10.1001/jama.2016.17216 http://doi.org/10.1001/jama.2016.17216 http://jama.jamanetwork.com/article.aspx?doi=10.1001/jama.2016.17216 http://jama.jamanetwork.com/article.aspx?doi=10.1001/jama.2016.17216

Quellec G , Charrière K , Boudi Y , et al . Deep image mining for diabetic retinopathy screening . Med Image Anal 2017 ; 39 : 178 - 93 . doi: DOI: 10.1016/j.media.2017.04.012 http://doi.org/10.1016/j.media.2017.04.012 https://linkinghub.elsevier.com/retrieve/pii/S136184151730066X https://linkinghub.elsevier.com/retrieve/pii/S136184151730066X

Yang Y , Li T , Li W , et al . Lesion detection and grading of diabetic retinopathy via two-stages deep convolutional neural networks. 2017 20th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). 2017 Sep 10-14; Quebec, Canada . Berlin: Springer ; 2017 . p. 533 - 40 . doi: DOI: 10.1007/978-3-319-66179-7_61 http://doi.org/10.1007/978-3-319-66179-7_61 .

Ardiyanto I , Nugroho HA , Rlb B . Deep learning-based diabetic retinopathy assessment on embedded system. 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (CMBC). 2017, Jul 11-15; Seogwipo, South Korea. Los Alamitos : IEEE Computer Society Press ; 2017 . p . 1760 - 3 . doi: DOI: 10.1109/EMBC.2017.8037184 http://doi.org/10.1109/EMBC.2017.8037184 .

Khojasteh P , Aliahmad B , Sridhar PA , et al. Introducing a novel layer in convolutional neural network for automatic identification of diabetic retinopathy. 2018 40th International Conference of the IEEE Engineering in Medicine and Biology Society (CMBC). 2018 Jul 18-21; Honolulu, USA. Los Alamitos : IEEE Computer Society Press ; 2018 . p. 5938 - 41 . doi: DOI: 10.1109/EMBC.2018.8513606 http://doi.org/10.1109/EMBC.2018.8513606 .

Gao ZT , Jie Li J , Guo JX , et al . Diagnosis of diabetic retinopathy using deep neural networks . IEEE Access 2019 ; 7 : 3360 - 70 . doi: DOI: 10.1109/ACCESS.2018.2888639 http://doi.org/10.1109/ACCESS.2018.2888639 https://ieeexplore.ieee.org/document/8581492/ https://ieeexplore.ieee.org/document/8581492/

Anonymous . FDA permits marketing of first US test labeled for simultaneous detection of tuberculosis bacteria and resistance to the antibiotic rifampin . Clin Infect Dis 2013 ; 57 ( 8 ):i-ii. doi: DOI: 10.1093/cid/cit548 http://doi.org/10.1093/cid/cit548 .

Fauw JD , Ledsam JR , Romera-Paredes B , et al . Clinically applicable deep learning for diagnosis and referral in retinal disease . Nature med 2018 ; 24 ( 9 ): 1342 - 50 . doi: . DOI: 10.1038/s41591-018-0107-6 http://doi.org/10.1038/s41591-018-0107-6

Eyenuk Inc . Receives health Canada approval for EyeArt™, an AI-enabled cloud-based automated diabetic retinopathy screening software . Business wire website . d. Published March 8, 2018 ; Accessed February 28 , 2019 . https://www.businesswire.com/news/home/20180308005485/en/Eyenuk-Receives-Health-Canada-Approval-EyeArt%E2%84%A2-AI-enable https://www.businesswire.com/news/home/20180308005485/en/Eyenuk-Receives-Health-Canada-Approval-EyeArt%E2%84%A2-AI-enable

Remidio Innovative Solutions . Smartphone-based retinal imaging together with artificial intelligence powers automated, sensitive and early detection of retinopathy . CISION PR Newswire Website . . Published March 12, 2018 ; accessed February 28 , 2019 . https://www.prnewswire.com/news-releases/smartphone-based-retinal-imaging-together-with-artificial-intelligence-powers-automated-sensitive-and-early-detection-of-retinopathy-676526933.html https://www.prnewswire.com/news-releases/smartphone-based-retinal-imaging-together-with-artificial-intelligence-powers-automated-sensitive-and-early-detection-of-retinopathy-676526933.html

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