Skip to main content
SpringerLink
Log in

Deep learning for diabetic retinopathy assessments: a literature review

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Diabetic retinopathy (DR) is the most important complication of diabetes. Early diagnosis by performing retinal image analysis helps avoid visual loss or blindness. A computer-aided diagnosis (CAD) system that uses images of the retinal fundus is an effective and efficient technique for the early diagnosis of diabetic retinopathy and helps specialists assess the disease. Many computer-aided diagnosis (CAD) systems have been developed to help in various stages like segmentation, detection and classification of lesions in fundus images. In the first way, the field actors have vocalized traditional machine learning (ML) techniques based on feature extraction and selection and then applied classification algorithms. The revolution of deep learning (DL) and its decisive victory over traditional ML methods for various applications motivated researchers to employ it for the diagnosis of DR and many deep learning-based methods have been introduced. In this article, we review these methods and highlight their pros and cons. We also talk about how hard it is to make deep learning methods that are good at diagnosing RD. So, our primary goal is to collaborate with experts to develop computer-aided diagnosis systems and test them in various hospital settings with varying picture quality. Finally, we highlight the remaining gaps and future research avenues to pursue.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Abbasi-Sureshjani S, Dashtbozorg B, Haar Romeny B, Fleuret F (2017) Boosted exudate segmentation in retinal images using residual nets. Fetal Infant And Ophthalmic Medical Image Analysis. pp 210–218

  2. Abràmoff M, Lou Y, Erginay A, Clarida W, Amelon R, Folk J, Niemeijer M (2016) Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Invest Ophthalmol Vis Sci 57:5200–5206

    Google Scholar 

  3. Abramoff M, Niemeijer M, Russell S (2010) Automated detection of diabetic retinopathy: barriers to translation into clinical practice. Expert Rev Med Devices 7:287–296

    Google Scholar 

  4. Adem K (2018) Exudate detection for diabetic retinopathy with circular hough transformation and convolutional neural networks. Exp Syst Appl 114:289–295

    Google Scholar 

  5. Akbar S et al (2021) A hybrid ensemble feature selection-based segmentation and deep majority voting framework on large multi-class diabetes retinopathy databases. Turk J Comput Math Educ (TURCOMAT) 12:416–428

    Google Scholar 

  6. Alyoubi W, Shalash W, Abulkhair M (2020) Diabetic retinopathy detection through deep learning techniques: a review. Inf Med Unlocked 20:100377

    Google Scholar 

  7. Ananda S, Kitahara D, Hirabayashi A, Reddy K (2019) Automatic fundus image segmentation for diabetic retinopathy diagnosis by multiple modified U-Nets and SegNets. In: 2019 Asia-Pacific Signal and information processing association annual summit and conference (APSIPA ASC), pp 1582–1588

  8. Anoop B et al (2022) Binary classification of DR-diabetic retinopathy using CNN with fundus colour images. Mater Today Proc 58:212–216

    Google Scholar 

  9. Asiri N, Hussain M, Al Adel F, Alzaidi N (2019) Deep learning based computer-aided diagnosis systems for diabetic retinopathy: a survey. Artif Intell Med 99:101701

    Google Scholar 

  10. Aziz T, Charoenlarpnopparut C, Mahapakulchai S (2023) Deep learning-based hemorrhage detection for diabetic retinopathy screening. Sci Rep 13:1479

    Google Scholar 

  11. Badar M, Haris M, Fatima A (2020) Application of deep learning for retinal image analysis: a review. Comput Sci Rev 35:100203

    MathSciNet  Google Scholar 

  12. Badar M, Shahzad M, Fraz M (2018) Simultaneous segmentation of multiple retinal pathologies using fully convolutional deep neural network. Ann Conf Med Image Underst Anal 313–324

  13. Bala R, Sharma A, Goel N (2022) A lightweight deep learning approach for diabetic retinopathy classification. In: Artificial intelligence and speech technology: third international conference, AIST 2021, Delhi, India, November 12–13, 2021. Revised Selected Papers. pp 277–287

  14. Bala R, Sharma A, Goel N (2022) Classification of fundus images for diabetic retinopathy using machine learning: a brief review. In: Proceedings Of academia-industry consortium for data science: AICDS, vol 2020. pp 37–45

  15. Biswal B, Prasanna T et al (2021) Robust segmentation of exudates from retinal surface using M-CapsNet via EM routing. Biomed Sig Process Control 68:102770

    Google Scholar 

  16. Bodapati J (2022) Stacked convolutional auto-encoder representations with spatial attention for efficient diabetic retinopathy diagnosis. Multimed Tools Appl 81:32033–32056

    Google Scholar 

  17. Bodapati J, Shaik N, Naralasetti V (2021) Composite deep neural network with gated-attention mechanism for diabetic retinopathy severity classification. J Ambient Intell Hum Comput 12:9825–9839

    Google Scholar 

  18. Budai A, Bock R, Maier A, Hornegger J, Michelson G (2013) Robust vessel segmentation in fundus images. International Journal Of Biomedical Imaging 2013

  19. Chen L, Magliano D, Zimmet P (2012) The worldwide epidemiology of type 2 diabetes mellitus—present and future perspectives. Nat Rev Endocrinol 8:228–236

    Google Scholar 

  20. Chen Y, Wu T, Wong W, Lee C (2018) Diabetic retinopathy detection based on deep convolutional neural networks. In: 2018 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 1030–1034

  21. Cho N, Shaw J, Karuranga S, Huang Y, Rocha Fernandes J, Ohlrogge A, Malanda B (2018) IDF diabetes atlas: global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes Res Clin Pract 138:271–281

    Google Scholar 

  22. Choi J, Yoo T, Seo J, Kwak J, Um T, Rim T (2017) Multi-categorical deep learning neural network to classify retinal images: a pilot study employing small database. PloS One 12:e0187336

    Google Scholar 

  23. Chudzik P, Majumdar S, Calivá F, Al-Diri B, Hunter A (2018) Microaneurysm detection using fully convolutional neural networks. Comput Methods Prog Biomed 158:185–192

    Google Scholar 

  24. Colas E, Besse A, Orgogozo A, Schmauch B, Meric N, Besse E (2016) Deep learning approach for diabetic retinopathy screening. Acta Ophthalmol 94

  25. 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 37:1149–1161

    Google Scholar 

  26. Das S, Saha S (2022) Diabetic retinopathy detection and classification using CNN tuned by genetic algorithm. Multimed Tools Appl 81:8007–8020

    Google Scholar 

  27. Decenciere E, Cazuguel G, Zhang X, Thibault G, Klein J, Meyer F, Marcotegui B, Quellec G, Lamard M, Danno R et al (2013) TeleOphta: machine learning and image processing methods for teleophthalmology. Irbm 34:196–203

    Google Scholar 

  28. Decencière E, Zhang X, Cazuguel G, Lay B, Cochener B, Trone C, Gain P, Ordonez R, Massin P, Erginay A et al (2014) Feedback on a publicly distributed image database: the Messidor database. Image Anal Stereol 33:231–234

    MATH  Google Scholar 

  29. Desika Vinayaki V, Kalaiselvi R (2023) ESLO: Enhanced sea lion optimization based bi-directional CNN-RNN for accurate detection of diabetic retinopathy. Concurrency And Computation: Practice And Experience e7391

  30. Detection A (2019) APTOS 2019 blindness detection. Accessed: 10 Jun 2019 https://www.kaggle.com/c/aptos2019-blindness-detection/

  31. Duan S, Huang P, Chen M, Wang T, Sun X, Chen M, Dong X, Jiang Z, Li D (2022) Semi-supervised classification of fundus images combined with CNN and GCN. Journal Of Applied Clinical Medical Physics e13746

  32. Dutta S, Manideep B, Basha S, Caytiles R, Iyengar N (2018) Classification of diabetic retinopathy images by using deep learning models. Int J Grid Distrib Comput 11:89–106

    Google Scholar 

  33. Eftekhari N, Pourreza H, Masoudi M, Ghiasi-Shirazi K, Saeedi E (2019) Microaneurysm detection in fundus images using a two-step convolutional neural network. Biomed Eng Online 18:1–16

    Google Scholar 

  34. El Hossi A, Skouta A, Elmoufidi A, Nachaoui M (2021) Applied CNN for automatic diabetic retinopathy assessment using fundus images. In: International conference on business intelligence, pp 425–433

  35. Elmoufidi A, Ammoun H (2022) Diabetic retinopathy prevention using efficientNetB3 architecture and fundus photography. SN Comput Sci 4:78

    Google Scholar 

  36. Elmoufidi A, Skouta A, Jai-Andaloussi S, Ouchetto O (2022) CNN with multiple inputs for automatic glaucoma assessment using fundus images. International Journal of Image and Graphics 2350012

  37. Elmoufidi A, Skouta A, Jai-andaloussi S, Ouchetto O (2022) Deep multiple instance learning for automatic glaucoma prevention and auto-annotation using color fundus photography. Prog Artif Intell 11:397–409

    Google Scholar 

  38. Erhan D, Courville A, Bengio Y, Vincent P (2010) Why does unsupervised pre-training help deep learning?. In: Proceedings of the thirteenth international conference on artificial intelligence and statistics, pp 201–208

  39. EyePACS dataset http://www.eyepacs.com/eyepacssystem/. Accessed 01 Mar 2018

  40. Faust O, Acharya R, Ng E, Ng K, Suri J (2012) Algorithms for the automated detection of diabetic retinopathy using digital fundus images: a review. J Med Syst 36:145–157

    Google Scholar 

  41. Fraz M, Remagnino P, Hoppe A, Uyyanonvara B, Rudnicka A, Owen C, Barman S (2012) Blood vessel segmentation methodologies in retinal images–a survey. Comput Methods Prog Biomed 108:407–433

    Google Scholar 

  42. Gargeya R, Leng T (2017) Automated identification of diabetic retinopathy using deep learning. Ophthalmology 124:962–969

    Google Scholar 

  43. Giancardo L, Meriaudeau F, Karnowski T, Li Y, Garg S, Tobin Jr K, Chaum E (2012) Exudate-based diabetic macular edema detection in fundus images using publicly available datasets. Med Image Anal 16:216–226

    Google Scholar 

  44. Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT press

  45. Gulshan V, Peng L, Coram M, Stumpe M, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J et al (2016) Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. Jama 316:2402–2410

    Google Scholar 

  46. Guo S, Li T, Kang H, Li N, Zhang Y, Wang K (2019) L-Seg: an end-to-end unified framework for multi-lesion segmentation of fundus images. Neurocomputing 349:52–63

    Google Scholar 

  47. Gupta A, Chhikara R (2018) Diabetic retinopathy: present and past. Procedia Comput Sci 132:1432–1440

    Google Scholar 

  48. Haloi M (2015) Improved microaneurysm detection using deep neural networks. ArXiv:1505.04424

  49. Harangi B, Toth J, Baran A, Hajdu A (2019) Automatic screening of fundus images using a combination of convolutional neural network and hand-crafted features. In: 2019 41st Annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp 2699–2702

  50. Hoover A, Kouznetsova V, Goldbaum M (2000) Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans Med Imaging 19:203–210

    Google Scholar 

  51. Hsu F (2002) Behind deep blue: building the computer that defeated the world chess champion. Princeton University Press

  52. Hua C, Huynh-The T, Lee S (2020) DRAN: densely reversed attention based convolutional network for diabetic retinopathy detection. In: 2020 42nd annual international conference of the ieee engineering in medicine & biology society (EMBC), pp 1992–1995

  53. Huang Y, Lin L, Li M, Wu J, Cheng P, Wang K, Yuan J, Tang X (2020) Automated hemorrhage detection from coarsely annotated fundus images in diabetic retinopathy. In: 2020 IEEE 17th International Symposium On Biomedical Imaging (ISBI), pp 1369–1372

  54. Ishtiaq U, Kareem S, Abdullah E, Mujtaba G, Jahangir R, Ghafoor H (2020) Diabetic retinopathy detection through artificial intelligent techniques: a review and open issues. Multimed Tools Appl 79:15209–15252

    Google Scholar 

  55. Islam M, Yang H, Poly T, Jian W, Li Y (2020) Deep learning algorithms for detection of diabetic retinopathy in retinal fundus photographs: a systematic review and meta-analysis. Comput Methods Prog Biomed 191:105320

    Google Scholar 

  56. Jena P, Khuntia B, Palai C, Nayak M, Mishra T, Mohanty S (2023) A novel approach for diabetic retinopathy screening using asymmetric deep learning features. Big Data Cogn Comput 7:25

    Google Scholar 

  57. Jiang H, Yang K, Gao M, Zhang D, Ma H, Qian W (2019) An interpretable ensemble deep learning model for diabetic retinopathy disease classification. In: 2019 41st annual international conference of the ieee engineering in medicine and biology society (EMBC), pp 2045–2048

  58. Johari M, Hassan H, Yassin A, Tahir N, Zabidi A, Rizman Z, Baharom R, Wahab N (2018) Early detection of diabetic retinopathy by using deep learning neural network. Int J Eng Technol 7:198–201

    Google Scholar 

  59. Jolad B, Khanai R (2023) An approach for speech enhancement with dysarthric speech recognition using optimization based machine learning frameworks. International Journal Of Speech Technology 1–19

  60. Kaggle Diabetic retinopathy detection https://www.kaggle.com/c/

  61. Kälviäinen R, Uusitalo H (2007) DIARETDB1 diabetic retinopathy database and evaluation protocol. Med Image Underst Anal 2007:61

    Google Scholar 

  62. Kandel I, Castelli M (2020) Transfer learning with convolutional neural networks for diabetic retinopathy image classification: a review. Appl Sci 10:2021

    Google Scholar 

  63. Kauppi T, Kalesnykiene V, Kamarainen J, Lensu L, Sorri I, Raninen A, Voutilainen R, Uusitalo H, Kälviäinen H, Pietilä J (2007) The diaretdb1 diabetic retinopathy database and evaluation protocol. BMVC 1:1–10

    Google Scholar 

  64. Kauppi T, Kalesnykiene V, Kamarainen J, Lensu L, Sorri I, Uusitalo H, Kälviäinen H, Pietilä J (2006) DIARETDB0: Evaluation database and methodology for diabetic retinopathy algorithms. Mach Vis Pattern Recog Res Group, Lappeenranta University of Technology, Finland 73:1–17

    Google Scholar 

  65. Kaur A, Dutta M, Soni K, Taneja N (2014) A secure and high payload digital audio watermarking using features from iris image. In: 2014 International conference on contemporary computing and informatics (IC3i). pp 509–512

  66. Kaur S, Goel N (2020) A Dilated Convolutional Approach for Inflammatory Lesion Detection Using Multi-Scale Input Feature Fusion (Workshop Paper). In: 2020 IEEE sixth international conference on multimedia big data (bigMM). pp 386–393

  67. Kavzoglu T, Sahin E, Colkesen I (2014) Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression. Landslides 11:425–439

    Google Scholar 

  68. Khan S, Abbas Z, Rizvi S et al (2019) Classification of diabetic retinopathy images based on customised CNN architecture. In: 2019 Amity international conference on artificial intelligence (AICAI), pp 244–248

  69. Kou C, Li W, Liang W, Yu Z, Hao J (2019) Microaneurysms segmentation with a U-Net based on recurrent residual convolutional neural network. J Med Imaging 6:025008–025008

    Google Scholar 

  70. Krizhevsky A, Sutskever I, Hinton G (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1097–1105

    Google Scholar 

  71. Kumar E, Bindu C (2021) Segmentation of retinal lesions in fundus images: a patch based approach using encoder-decoder neural network. In: 2021 7th International conference on advanced computing and communication systems (ICACCS), vol 1. pp 1247–1253

  72. Lahmar C, Idri A (2022) Deep hybrid architectures for diabetic retinopathy classification. Computer Methods In Biomechanics And Biomedical Engineering: Imaging & Visualization 1–19

  73. Lam C, Yi D, Guo M, Lindsey T (2018) Automated detection of diabetic retinopathy using deep learning. AMIA Summits Trans Sci Proc 2018:147

    Google Scholar 

  74. Lam C, Yu C, Huang L, Rubin D (2018) Retinal lesion detection with deep learning using image patches. Investig Ophthalmol Vis Sci 59:590–596

    Google Scholar 

  75. Latha D, Bell T, Sheela C (2022) Red lesion in fundus image with hexagonal pattern feature and two-level segmentation. Multimed Tools Appl 81:26143–26161

    Google Scholar 

  76. LeCun Y, Bengio Y, Hinton G (2015) Deep learning, vol 521

  77. LeCun Y, Boser B, Denker J, Henderson D, Howard R, Hubbard W, Jackel L (1989) Backpropagation applied to handwritten zip code recognition. Neural Comput 1:541–551

    Google Scholar 

  78. Li T, Bo W, Hu C, Kang H, Liu H, Wang K, Fu H (2021) Applications of deep learning in fundus images: a review. Medical Image Analysis 101971

  79. Li T, Gao Y, Wang K, Guo S, Liu H, Kang H (2019) Diagnostic assessment of deep learning algorithms for diabetic retinopathy screening. Inf Sci 501:511–522

    Google Scholar 

  80. Li X, Hu X, Yu L, Zhu L, Fu C, Heng P (2019) CANet: cross-disease attention network for joint diabetic retinopathy and diabetic macular edema grading. IEEE Trans Med Imaging 39:1483–1493

    Google Scholar 

  81. Li X, Pang T, Xiong B, Liu W, Liang P, Wang T (2017) Convolutional neural networks based transfer learning for diabetic retinopathy fundus image classification. In: 2017 10th International congress on image and signal processing, Biomedical Engineering And Informatics (CISP-BMEI), pp 1–11

  82. Liang Z, Bin X, Yong K et al (2006) Information identification technology. Mechanical Industry Press, New York

    Google Scholar 

  83. Lin Z, Guo R, Wang Y, Wu B, Chen T, Wang W, Chen D, Wu J (2018) A framework for identifying diabetic retinopathy based on anti-noise detection and attention-based fusion. In: Medical Image Computing And Computer Assisted Intervention–MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part II 11, pp 74–82

  84. Liu Y, Li Z, Xu C, Li J, Liang R (2019) Referable diabetic retinopathy identification from eye fundus images with weighted path for convolutional neural network. Artif Intell Med 99:101694

    Google Scholar 

  85. Lotfy M, Adeghate J, Kalasz H, Singh J, Adeghate E (2017) Chronic complications of diabetes mellitus: a mini review. Curr Diabetes Rev 13:3–10

    Google Scholar 

  86. Mansour R (2018) Deep-learning-based automatic computer-aided diagnosis system for diabetic retinopathy. Biomed Eng Lett 8:41–57

    Google Scholar 

  87. Marín D, Aquino A, Gegúndez-Arias M, Bravo J (2010) A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features. IEEE Trans Med Imaging 30:146–158

    Google Scholar 

  88. Masood S, Luthra T, Sundriyal H, Ahmed M (2017) Identification of diabetic retinopathy in eye images using transfer learning. In: 2017 International conference on computing, communication and automation (ICCCA), pp 1183–1187

  89. Mayya V, Kamath S, Kulkarni U (2021) Automated microaneurysms detection for early diagnosis of diabetic retinopathy: a comprehensive review. Comput Methods Prog Biomed Update 1:100013

    Google Scholar 

  90. Nahiduzzaman M, Islam M, Goni M, Anower M, Ahsan M, Haider J, Kowalski M (2023) Diabetic retinopathy identification using parallel convolutional neural network based feature extractor and ELM classifier. Expert Systems With Applications 119557

  91. Nazir T, Irtaza A, Javed A, Malik H, Hussain D, Naqvi R (2020) Retinal image analysis for diabetes-based eye disease detection using deep learning. Appl Sci 10:6185

    Google Scholar 

  92. Neff T, Payer C, Stern D, Urschler M (2017) Generative adversarial network based synthesis for supervised medical image segmentation. Proc OAGM ARW Joint Workshop 3:4

    Google Scholar 

  93. Niemeijer M, Van Ginneken B, Cree M, Mizutani A, Quellec G, Sánchez C, Zhang B, Hornero R, Lamard M, Mur matsu C et al (2009) Retinopathy online challenge: automatic detection of microaneurysms in digital color fundus photographs. IEEE Trans Med Imaging 29:185–195

    Google Scholar 

  94. Nirthika R, Manivannan S, Ramanan A (2020) Loss functions for optimizing Kappa as the evaluation measure for classifying diabetic retinopathy and prostate cancer images. In: 2020 IEEE 15th international conference on industrial and information systems (ICIIS), pp 144–149

  95. Okur M, Karantas I, Siafaka P (2017) Diabetes Mellitus: a review on pathophysiology, current status of oral pathophysiology, current status of oral medications and future perspectives. ACTA Pharmaceutica Sciencia 55

  96. Orlando J, Prokofyeva E, Del Fresno M, Blaschko M (2018) An ensemble deep learning based approach for red lesion detection in fundus images. Comput Methods Prog Biomed 153:115–127

    Google Scholar 

  97. Perdomo O, Arevalo J, González F (2017) Convolutional network to detect exudates in eye fundus images of diabetic subjects. In: 12th International Symposium On Medical Information Processing And Analysis, 10160 pp 101600T

  98. Pires R, Avila S, Wainer J, Valle E, Abramoff M, Rocha A (2019) A data-driven approach to referable diabetic retinopathy detection. Artif Intell Med 96:93–106

    Google Scholar 

  99. Pires R, Jelinek H, Wainer J, Valle E, Rocha A (2014) Advancing bag-of-visual-words representations for lesion classification in retinal images. PloS One 9:e96814

    Google Scholar 

  100. Playout C, Duval R, Cheriet F (2018) A multitask learning architecture for simultaneous segmentation of bright and red lesions in fundus images. In: International conference on medical image computing and computer-assisted intervention, pp 101–108

  101. Playout C, Duval R, Cheriet F (2019) A novel weakly supervised multitask architecture for retinal lesions segmentation on fundus images. IEEE Trans Med Imaging 38:2434–2444

    Google Scholar 

  102. Porwal P, Pachade S, Kamble R, Kokare M, Deshmukh G, Sahasrabuddhe V (2018) Andamp; Meriaudeau, F. Indian diabetic retinopathy image dataset (IDRid): a database for diabetic retinopathy screening research. Data. 3:25

    Google Scholar 

  103. 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 3:25

    Google Scholar 

  104. Pratt H, Coenen F, Broadbent D, Harding S, Zheng Y (2016) Convolutional neural networks for diabetic retinopathy. Proc Comput Sci 90:200–205

    Google Scholar 

  105. Prentašić P, Lončarić S (2016) Detection of exudates in fundus photographs using deep neural networks and anatomical landmark detection fusion. Comput Methods Prog Biomed 137:281–292

    Google Scholar 

  106. Prentašić P, Lončarić S, Vatavuk Z, Benčić G, Subašić M, Petković T, Dujmović L, Malenica-Ravlić M, Budimlija N, Tadić R (2013) Diabetic retinopathy image database (DRiDB): a new database for diabetic retinopathy screening programs research. In: 2013 8th International symposium on image and signal processing and analysis (ISPA). pp 711–716

  107. Qian Z, Wu C, Chen H, Chen M (2021) Diabetic retinopathy grading using attention based convolution neural network. In: 2021 IEEE 5th advanced information technology, electronic and automation control conference (IAEAC), vol 5. pp 2652–2655

  108. Qomariah D, Tjandrasa H, Fatichah C (2022) Exudate segmentation for diabetic retinopathy using modified FCN-8 and dice loss. International Journal Of Intelligent Engineering And Systems 15

  109. Quellec G, Charrière K, Boudi Y, Cochener B, Lamard M (2017) Deep image mining for diabetic retinopathy screening. Med Image Anal 39:178–193

    Google Scholar 

  110. Quellec G, Lamard M, Josselin P, Cazuguel G, Cochener B, Roux C (2008) Optimal wavelet transform for the detection of microaneurysms in retina photographs. IEEE Trans Med Imaging 27:1230–1241

    Google Scholar 

  111. Raman V, Then P, Sumari P (2016) Proposed retinal abnormality detection and classification approach: computer aided detection for diabetic retinopathy by machine learning approaches. In: 2016 8th IEEE International conference on communication software and networks (ICCSN). pp 636–641

  112. Riaz H, Park J, Choi H, Kim H, Kim J (2020) Deep and densely connected networks for classification of diabetic retinopathy. Diagnostics 10:24

    Google Scholar 

  113. Sadek I, Sidibé D, Meriaudeau F (2015) Automatic discrimination of color retinal images using the bag of words approach. Medical Imaging 2015: Computer-aided Diagnosis 9414:94141J

    Google Scholar 

  114. Saha O, Sathish R, Sheet D (2019) Fully convolutional neural network for semantic segmentation of anatomical structure and pathologies in colour fundus images associated with diabetic retinopathy. ArXiv:1902.03122

  115. Salam A, Mahadevappa M, Das A, Nair M (2022) DRG-NET: a graph neural network for computer-aided grading of diabetic retinopathy. Sig Image Video Process 16:1869–1875

    Google Scholar 

  116. Sambyal N, Saini P, Syal R, Gupta V (2020) Modified U-Net architecture for semantic segmentation of diabetic retinopathy images. Biocybernet Biomed Eng 40:1094–1109

    Google Scholar 

  117. Sarhan M, Albarqouni S, Yigitsoy M, Navab N, Eslami A (2019) Multi-scale microaneurysms segmentation using embedding triplet loss. In: International conference on medical image computing and computer-assisted intervention, pp 174–182

  118. Sayres R, Taly A, Rahimy E, Blumer K, Coz D, Hammel N, Krause J, Narayanaswamy A, Rastegar Z, Wu D et al (2019) Using a deep learning algorithm and integrated gradients explanation to assist grading for diabetic retinopathy. Ophthalmology 126:552–564

    Google Scholar 

  119. Schawinski K, Zhang C, Zhang H, Fowler L, Santhanam G (2017) Generative adversarial networks recover features in astrophysical images of galaxies beyond the deconvolution limit. Mon Not R Astron Soc Lett 467:L110–L114

    Google Scholar 

  120. Scherer D, Müller A, Behnke S (2010) Evaluation of pooling operations in convolutional architectures for object recognition. In: International conference on artificial neural networks, pp 92–101

  121. Selçuk T, Beyoğlu A, Alkan A (2022) Automatic detection of exudates and hemorrhages in low-contrast color fundus images using multi semantic convolutional neural network. Concurr Comput Pract Experience 34:e6768

    Google Scholar 

  122. Shaik N, Cherukuri T (2022) Hinge attention network: a joint model for diabetic retinopathy severity grading. Appl Intell 52:15105–15121

    Google Scholar 

  123. Shanthi T, Sabeenian R (2019) Modified Alexnet architecture for classification of diabetic retinopathy images. Comput Electr Eng 76:56–64

    Google Scholar 

  124. Sivaprasad S, Arden G, Prevost A, Crosby-Nwaobi R, Holmes H, Kelly J, Murphy C, Rubin G, Vasconcelos J, Hykin P (2014) A multicentre phase III randomised controlled single-masked clinical trial evaluating the cl inical e fficacy and safety of light-masks at p reventing dark-a daptation in the tr eatment of ea rly diabetic macular oedema (CLEOPATRA): study protocol for a randomised controlled trial. Trials 15:1–10

    Google Scholar 

  125. Sivaswamy J, Krishnadas S, Joshi G, Jain M, Tabish A (2014) Drishti-gs: retinal image dataset for optic nerve head (onh) segmentation. In: 2014 IEEE 11th international symposium on biomedical imaging (ISBI). pp 53–56

  126. Skouta A, Elmoufidi A, Jai-Andaloussi S, Ochetto O (2021) Automated binary classification of diabetic retinopathy by convolutional neural networks. Advances On Smart And Soft Computing 177–187

  127. Skouta A, Elmoufidi A, Jai-Andaloussi S, Ouchetto O (2022) Hemorrhage semantic segmentation in fundus images for the diagnosis of diabetic retinopathy by using a convolutional neural network. J Big Data 9:1–24

    Google Scholar 

  128. Skouta A, Elmoufidi A, Jai-Andaloussi S, Ouchetto O (2022) Semantic segmentation of retinal blood vessels from fundus images by using CNN and the random forest algorithm. SENSORNETS 163–170

  129. Sopharak A, Uyyanonvara B, Barman S, Williamson T (2008) Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods. Comput Med Imaging Graph 32:720–727

    Google Scholar 

  130. Sun K, He M, Xu Y, Wu Q, He Z, Li W, Liu H, Pi X (2022) Multi-label classification of fundus images with graph convolutional network and LightGBM. Comput Biol Med 149:105909

    Google Scholar 

  131. Tan J, Fujita H, Sivaprasad S, Bhandary S, Rao A, Chua K, Acharya U (2017) Automated segmentation of exudates, haemorrhages, microaneurysms using single convolutional neural network. Inf Sci 420:66–76

    Google Scholar 

  132. Tsighe Hagos M, Kant S (2019) Transfer learning based detection of diabetic retinopathy from small dataset. ArXiv:arXiv-1905

  133. Tymchenko B, Marchenko P, Spodarets D (2020) Deep learning approach to diabetic retinopathy detection. ArXiv:2003.02261

  134. Van Grinsven M, Ginneken B, Hoyng C, Theelen T, Sánchez C (2016) Fast convolutional neural network training using selective data sampling: application to hemorrhage detection in color fundus images. IEEE Trans Med Imaging 35:1273–1284

    Google Scholar 

  135. Walter T, Klein J, 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 21:1236–1243

    Google Scholar 

  136. Wan S, Liang Y, Zhang Y (2018) Deep convolutional neural networks for diabetic retinopathy detection by image classification. Comput Electr Eng 72:274–282

    Google Scholar 

  137. Wang L, Chen Z, Wang M, Wang T, Zhu W, Chen X (2021) Cycle adaptive multi-target weighting network for automated diabetic retinopathy segmentation. In: 2021 IEEE 18th international symposium on biomedical imaging (ISBI). pp 1141–1144

  138. Wang X, Lu Y, Wang Y, Chen W (2018) Diabetic retinopathy stage classification using convolutional neural networks. In: 2018 IEEE International conference on information reuse and integration (IRI), pp 465–471

  139. Wang J, Luo J, Liu B, Feng R, Lu L, Zou H (2020) Automated diabetic retinopathy grading and lesion detection based on the modified r-FCN object-detection algorithm. IET Comput Vis 14:1–8

    Google Scholar 

  140. Wang Z, Yin Y, Shi J, Fang W, Li H, Wang X (2017) Zoom-in-net: deep mining lesions for diabetic retinopathy detection. In: International conference on medical image computing and computer-assisted intervention, pp 267–275

  141. Wang H, Yuan G, Zhao X, Peng L, Wang Z, He Y, Qu C, Peng Z (2020) Hard exudate detection based on deep model learned information and multi-feature joint representation for diabetic retinopathy screening. Comput Methods Prog Biomed 191:105398

    Google Scholar 

  142. Worrall D, Wilson C, Brostow G (2016) Automated retinopathy of prematurity case detection with convolutional neural networks. Deep Learning And Data Labeling For Medical Applications 68–76

  143. Xu B, Wang N, Chen T, Li M (2015) Empirical evaluation of rectified activations in convolutional network. ArXiv:1505.00853

  144. Xue J, Yan S, Qu J, Qi F, Qiu C, Zhang H, Chen M, Liu T, Li D, Liu X (2019) Deep membrane systems for multitask segmentation in diabetic retinopathy. Knowl-Based Syst 183:104887

    Google Scholar 

  145. Yan Y, Gong J, Liu Y (2019) A novel deep learning method for red lesions detection using hybrid feature. In: 2019 Chinese control and decision conference (CCDC), pp 2287–2292

  146. Yan Z, Han X, Wang C, Qiu Y, Xiong Z, Cui S (2019) Learning mutually local-global U-Nets for high-resolution retinal lesion segmentation in fundus images. In: 2019 IEEE 16th international symposium on biomedical imaging (ISBI), vol 2019, pp 597–600

  147. Zago G, Andreão R, Dorizzi B, Salles E (2020) Diabetic retinopathy detection using red lesion localization and convolutional neural networks. Comput Biol Med 116:103537

    Google Scholar 

  148. Zeng X, Chen H, Luo Y, Ye W (2019) Automated diabetic retinopathy detection based on binocular siamese-like convolutional neural network. IEEE Access 7:30744–30753

    Google Scholar 

  149. Zhang G, Sun B, Chen Z, Gao Y, Zhang Z, Li K, Yang W (2022) Diabetic retinopathy grading by deep graph correlation network on retinal images without manual annotations. Front Med 9

  150. Zhang Z, Yin F, Liu J, Wong W, Tan N, Lee B, Cheng J, Wong T (2010) Origa-light: an online retinal fundus image database for glaucoma analysis and research. In: 2010 Annual international conference of the ieee engineering in medicine and biology. pp 3065–3068

  151. Zhang W, Zhong J, Yang S, Gao Z, Hu J, Chen Y, Yi Z (2019) Automated identification and grading system of diabetic retinopathy using deep neural networks. Knowl-Based Syst 175:12–25

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, Computer and Systems Laboratory, Faculty of Sciences Ain CHOCK, Hassan II University, Casablanca, 20100, Morocco

    Ayoub Skouta, Said Jai-Andaloussi & Ouail Ouchetto

  2. Department of Mathematics and Computer Science, Data4Earth Laboratory, Sciences and Technologies Faculty, Sultan Moulay Slimane University, Beni Mellal, 523, Morocco

    Abdelali Elmoufidi

Authors
  1. Ayoub Skouta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abdelali Elmoufidi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Said Jai-Andaloussi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ouail Ouchetto
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ayoub Skouta.

Ethics declarations

Conflict of Interests

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix: : Nomenclature

Appendix: : Nomenclature

Table 11
Full size table
Table 12
Full size table

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Skouta, A., Elmoufidi, A., Jai-Andaloussi, S. et al. Deep learning for diabetic retinopathy assessments: a literature review. Multimed Tools Appl 82, 41701–41766 (2023). https://doi.org/10.1007/s11042-023-15110-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-15110-9

Keywords

Search

Navigation