Abstract
Diabetic retinopathy (DR) is an impediment of diabetes mellitus, which if not treated early may result in complete loss of vision, even without any preemptive symptoms. DR is caused by high level of glucose in the blood, causing alterations in the microvasculature of retina. However, early screening of diabetic patients through retinal fundus imaging, along with proper diagnosis and treatment can control the prevalence of DR complications. Manual inspection of pathological changes in retinal fundus images is an extremely challenging and tedious task. Therefore, computer-aided diagnosis (CAD) system is an efficient and effective method for early detection of DR and can greatly assist the ophthalmologists. CAD system encompasses DR detection and severity grading that includes detection, classification, localization and segmentation of lesions from the fundus images. Significant contributions have been made in DR severity grading using conventional image processing approaches using hand-engineered features and traditional machine-learning (ML) techniques. In the recent years, significant development of deep learning (DL) methods alleviated by the advancement of hardware computation power and efficient learning algorithms, has triumphed over the traditional ML methods in DR detection and grading tasks. Many researchers have employed the established as well as customized DL models in different DR image repositories and reported their findings. In this paper, we conduct a detailed review of the recent state-of-the-art contributions in the field of DL based DR classification by explaining their methodologies and highlighting their advantages and limitations. A detailed comparative study based on certain statistical parameters has also been conducted to quantitatively evaluate the methods, models and preprocessing techniques. In addition, the challenges in designing an efficient, accurate and robust deep-learning model for DR classification are explored in details to help the future research in this field.
Similar content being viewed by others
Data availability
Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.
References
Wang RY, Zhang RP, Chen J, Sun R, Yang XY, Ke H, Chen DH (2013) Cai, Prevalence and risk factors for diabetic retinopathy in a high-risk Chinese population. BMC Public Health 13:633. https://doi.org/10.1186/1471-2458-13-633
Sussman EJ, Tsiaras WG, Soper KA (1982) Diagnosis of diabetic eye disease. JAMA Ophthalmol 247(23):3231–3234
Whiting DR, Guariguata L, Weil C, Shaw J (2011) IDF diabetes atlas: global estimates of the prevalence of diabetes for 2011 and 2030. Diabetes Res Clin Pract 94(3):311–321. https://doi.org/10.1016/j.diabres.2011.10.029
Keenan TD, Johnston RL, Donachie PH, Sparrow JM, Stratton IM, Scanlon P (2013) United Kingdom National Ophthalmology Database Study: Diabetic Retinopathy; Report 1: prevalence of centre-involving diabetic macular edema and other grades of maculopathy and retinopathy in hospital eye services. Eye (London) 27:1397–1404
Klein R, Klein BE, Moss SE, Davis MD, DeMets DL (1984) The wisconsin epidemiologic study of diabetic retinopathy II. Prevalence and risk of diabetic retinopathy when age at diagnosis is less than 30 years. Arch Ophthalmol 102:520–526
Klein R, Klein BE, Moss SE, Davis MD, DeMets DL (1984) The Wisconsin epidemiologic study of diabetic retinopathy III. Prevalence and risk of diabetic retinopathy when age at diagnosis is 30 or more years. Arch Ophthalmol 102:527–532
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 27:1047–1053
Kannan R (2019) India is home to 77 million diabetics, second highest in the world. The Hindu. ISSN 0971–751X. https://www.thehindu.com/sci-tech/health/india-has-second-largest-number-of-people-with-diabetes/article29975027.ece. Accessed 29 Apr 2020
Migiro G (2018) Countries by Percentage of World Population, WorldAtlas. https://www.worldatlas.com/articles/countries-by-percentage-of-world-population.html. Accessed 19 May 2020
Bhutia KL, Lomi N, Bhutia SC (2017) Prevalence of diabetic retinopathy in type 2 diabetic patients attending tertiary care hospital in sikkim. DJO 2017 28:19–21
Rema M, Premkumar S, Anitha B, Deepa R, Pradeepa R, Mohan V (2005) Prevalence of diabetic retinopathy in urban India: The Chennai urban rural epidemiology study (CURES) eye study. I Invest Ophthalmol Vis Sci 46:2328–2333. https://doi.org/10.1167/iovs.05-0019
Rema M, Pradeepa R (2007) Diabetic retinopathy: An Indian perspective. Indian J Med Res 125:297–310
Raman R, Rani PK, Reddi Rachepalle S, Gnanamoorthy P, Uthra S, Kumaramanickavel G, Sharma T (2009) Prevalence of diabetic retinopathy in India: Sankara Nethralaya Diabetic Retinopathy Epidemiology and Molecular Genetics Study report 2. Ophthalmol 116(2):311–318. https://doi.org/10.1016/j.ophtha.2008.09.010
Dr. Rajendra Prasad Centre for Ophthalmic Sciences et al (2019) AIIMS, New Delhi, National Diabetes and Diabetic Retinopathy Survey India 2015–2019 – A Summary Report, National Programme for Control of Blindness &Visual Impairment, Directorate General of Health Services, Ministry of Health & Family Welfare, Government of India, New Delhi, 2019, .https://npcbvi.gov.in/writeReadData/mainlinkFile/File342.pdf Accessed: 2020–07–14
Looker HC, Nyangoma SO, Cromie D, Olson JA, Leese GP, Black M, Doig J, Lee N, Lindsay RS, McKnight JA, Morris AD, Philip S, Sattar N, Wild SH, Colhoun HM, Scottish Diabetic Retinopathy Screening Collaborative, Scottish Diabetes Research Network Epidemiology Group (2012) Diabetic retinopathy at diagnosis of type 2 diabetes in Scotland. Diabetologia 55(9):2335–2342. https://doi.org/10.1007/s00125-012-2596-z
Lee SC, Lee ET, Kingsley RM, Wang Y, Russell D, Klein R, Wanr A (2001) Comparison of diagnosis of early retinal lesions of diabetic retinopathy between a computer system and human experts. Arch Ophthalmol (Chicago, Ill.: 1960) 119(4):509–515. https://doi.org/10.1001/archopht.119.4.509
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. https://doi.org/10.1016/j.artmed.2019.07.009
Qureshi I, Ma J, Abbas Q (2019) Recent development on detection methods for the diagnosis of diabetic retinopathy. Symmetry 11:749
Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, van der Laak JAWM, van Ginneken B, Sánchez CI (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88. https://doi.org/10.1016/j.media.2017.07.005
Kauppi T, Kälviäinen H (2008) Simple and robust optic disc localisation using colour decorrelated templates. In: Blanc-Talon J, Bourennane S, Philips W, Popescu D, Scheunders P (eds) Advanced Concepts for Intelligent Vision Systems, ACIVS 2008, Lecture Notes in Computer Science, vol 5259. Springer-Verlag, Heidelberg, pp 719–729. https://doi.org/10.1007/978-3-540-88458-3_65
Sopharak A, Uyyanonvara B, Barman S, Williamson TH (2008) Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods. Comput Med Imaging Graph 32(8):720–727. https://doi.org/10.1016/j.compmedimag.2008.08.009
Jonas JB, Gusek GC, Naumann GOH (1988) Optic disk morphometry in high myopia. Graefes Arch Clin Exp Ophthalmol 226(6):587–590. https://doi.org/10.1007/BF02169209
Joshi GD, Sivaswamy J, Krishnadas SR (2011) Optic disk and cup segmentation from monocular color retinal images for glaucoma assessment. IEEE Trans Med Imaging 30(6):1192–1205. https://doi.org/10.1109/TMI.2011.2106509
Hubbard LD, Brothers RJ, King WN, Clegg LX, Klein R, Cooper LS, Sharrett AR, Davis MD, Cai J (1999) Methods for evaluation of retinal microvascular abnormalities associated with hypertension/sclerosis in the atherosclerosis risk in communities study. Ophthalmology 106(12):2269–2280. https://doi.org/10.1016/S0161-6420(99)90525-0
Knudtson MD, Lee KE, Hubbard LD, Wong TY, Klein R, Klein BE (2003) Revised formulas for summarizing retinal vessel diameters. Curr Eye Res 27(3):143–149. https://doi.org/10.1076/ceyr.27.3.143.16049
Liu J, Wong DWK, Lim JH, Li H, Tan NM, Zhang Z, Wong TY, Lavanya R (2009) ARGALI: An automatic cup-to-disc ratio measurement system for glaucoma analysis using level-set image processing. In: Lim CT, Goh JCH (eds) 13th International Conference on Biomedical Engineering, IFMBE Proceedings, vol 23. Springer, Heidelberg, pp 559–562. https://doi.org/10.1007/978-3-540-92841-6_137
Hatanaka Y, Noudo A, Muramatsu C, Sawada A, Hara T, Yamamoto T, Fujita H (2010) Automatic measurement of vertical cup-to-disc ratio on retinal fundus images. In: Zhang D, Sonka M (eds) Medical Biometrics. ICMB 2010. Lecture Notes in Computer Science, vol 6165. Springer, Heidelberg, pp 64–72. https://doi.org/10.1007/978-3-642-13923-9_7
Gagnon L, Lalonde M, Beaulieu M, Boucher MC (2001) Procedure to detect anatomical structures in optical fundus images. In Proc SPIE Conf Med Imaging 4322:1218–1225. https://doi.org/10.1117/12.430999
Brown BA, Williams H, George SJ (2017) Chapter six - evidence for the involvement of matrix-degrading metalloproteinases (MMPs) in atherosclerosis. In: Khalil RA (ed) Progress in Molecular Biology and Translational Science, vol 147. Academic Press, pp 197–237. https://doi.org/10.1016/bs.pmbts.2017.01.004
Early Treatment Diabetic Retinopathy Study Research Group et al (1991) Grading diabetic retinopathy from stereoscopic color fundus photographs–an extension of the modified Airlie House classification. Ophthalmology 98(5 Suppl):786–806 (ETDRS report number 10)
Schmidt D, McLeod D (2007) Cotton wool spots should not be regarded as retinal nerve fibre layer infarcts. Eur J Med Res 12(4):179–180
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 45:161–171. https://doi.org/10.1016/j.compbiomed.2013.11.014
Lee T-Y, Cheng HD (1994) Parallel grading of venous beading on transputer, In Proceedings of 1994 20th Annual Northeast Bioengineering Conference, pp 54–58, Springfield, MA, USA. https://doi.org/10.1109/NEBC.1994.305177
Patz A (1980) Studies on retinal neovascularization. friedenwald lecture. Invest Ophthalmol Vis Sci 19(10):1133–1138
Diabetic retinopathy, https://www.nhs.uk/conditions/diabetic-retinopathy/stages/. Accessed: 2020–07–17
Early Treatment Diabetic Retinopathy Study Research Group et al (1987) Treatment techniques and clinical guidelines for photocoagulation of diabetic macular edema: Early treatment diabetic retinopathy study report number 2. Ophthalmology 94(7):761–774. https://doi.org/10.1016/s0161-6420(87)33527-4
Acharya UR, Lim CM, Ng EY, Chee C, Tamura T (2009) Computer-based detection of diabetes retinopathy stages using digital fundus images. Proceedings of the institution of mechanical engineers, In Proceedings of the Institution of Mechanical Engineers. Part H, J Eng Med 223(5):545–53. https://doi.org/10.1243/09544119JEIM486
Fleming AD, Goatman KA, Philip S, Williams GJ, Prescott GJ, Scotland GS, McNamee P, Leese GP, Wykes WN, Sharp PF, Olson JA, Scottish Diabetic Retinopathy Clinical Research Network (2010) The role of haemorrhage and exudate detection in automated grading of diabetic retinopathy. Br J Ophthalmol 94(6):706–711. https://doi.org/10.1136/bjo.2008.149807
Kanski JJ (2009) Clinical ophthalmology: a synopsis, 2nd edn. Butterworth-Heinemann, Elsevier
Razzak MI, Naz S, Zaib A (2018) Deep learning for medical image processing: Overview, challenges and the future. In: Dey N, Ashour A, Borra S (eds) Classification in BioApps, Lecture Notes in Computational Vision and Biomechanics, vol 26. Springer, Cham. https://doi.org/10.1007/978-3-319-65981-7_12
Zachariah S, Wykes W, Yorston D (2015) Grading diabetic retinopathy (DR) using the Scottish grading protocol. Commun Eye Health 28(92):72–73
(2015) Diabetic retinopathy (DR): management and referral. In Community Eye Health 28(92):70–71. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4944098/
Decencière E, Zhang X, Cazuguel G, Lay B, Cochener B, Trone C, Gain P, Ordonez R, Massin P, Erginay A, Charton B, Klein JC (2014) Feedback on a publicly distributed database: the Messidor database. Image Anal Stereol 33(3):231–234. https://doi.org/10.5566/ias.1155
Abràmoff MD, Folk JC, Han DP, Walker JD, Williams DF, Russell SR, Massin P, Cochener B, Gain P, Tang L, Lamard M, Moga DC, Quellec G, Niemeijer M (2013) Automated analysis of retinal images for detection of referable diabetic retinopathy. JAMA Ophthalmology 131(3):351–357. https://doi.org/10.1001/jamaophthalmol.2013.1743
Messidor and Messidor 2 dataset. Available at: http://www.adcis.net/en/third-party/messidor/ and http://www.adcis.net/en/third-party/messidor2/. [Accessed: 2020–06–12]
Decencière E, Cazuguel V, Zhang X, Thibault G, Klein J-C, Meyer F, Marcotegui B, Quellec G, Lamard M, Danno R, Elie D, Massin P, Viktor Z, Erginay A, Laÿ B, Chabouis A (2013) TeleOphta: Machine learning and image processing methods for teleophthalmology. IRBM 34(2):196–203. https://doi.org/10.1016/j.irbm.2013.01.010. Available: http://www.adcis.net/en/Download-Third-Party/E-Ophtha.html. [Accessed: 2020–06–12]
Kaggle diabetic retinopathy detection competition: Kaggle EyePACS dataset, Available. https://www.kaggle.com/c/diabetic-retinopathy-detection/data. [Accessed: 2020–06–12]
Kaggle APTOS 2019 Blindness Detection competition, Available. https://www.kaggle.com/c/aptos2019-blindness-detection/data. [Accessed: 2020–06–12]
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. Available: https://ieee-dataport.org/open-access/indian-diabetic-retinopathy-image-dataset-idrid. [Accessed: Jul. 20, 2020]
Ferri C, Hernández-Orallo J, Modroiu R (2009) An experimental comparison of performance measures for classification. Pattern Recogn Lett 30(1):27–38. https://doi.org/10.1016/j.patrec.2008.08.010
Duchi J, Hazan E, Singer Y (2011) Adaptive subgradient methods for online learningand stochastic optimization. J Mach Learn Res 12:2121–2159
Zeiler MD (2012) ADADELTA: An adaptive learning rate method. arXiv preprint arXiv:1212.5701
Mcmahan HB, Streeter M (2014) Delay-tolerant algorithms for asynchronous distributed online learning. Adv Neural Inf Process Syst (Proceedings of NIPS), pp 1–9
Kingma DP, Ba JL (2015) Adam: a Method for Stochastic Optimization. In: International Conference on Learning Representations 2015, pp 1–13
Dozat T (2016) Incorporating nesterov momentum into adam. ICLR Workshop 1:2013–2016
Kumar SK (2017) On weight initialization in deep neural networks. CoRR, abs/1704.08863
He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: Surpassing human-level performance on imageNet classification. 2015 IEEE International Conference on Computer Vision (ICCV) (vol. 1, pp 1026-34). Santiago, Chile. https://doi.org/10.1109/iccv.2015.123
Mishkin D, Matas J (2015) All you need is a good init. arXiv preprint arXiv:1511.06422
Ioffe S, Szegedy C (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift. In Proceedings of the 32nd International Conference on International Conference on Machine Learning - Volume 37 (ICML'15). JMLR.org, pp. 448–456. https://doi.org/10.5555/3045118.3045167
Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. In: Fleet D, Pajdla T, Schiele B, Tuytelaars T (eds) Computer Vision – ECCV 2014. ECCV 2014, Lecture notes in computer science, vol 8689. Springer, Cham. https://doi.org/10.1007/978-3-319-10590-1_53
Oquab M, Bottou L, Laptev I, Sivic J (2014) Learning and transferring mid-level image representations using convolutional neural networks, IEEE Conference On Computer Vision And Pattern Recognition (CVPR-2014), Columbus, OH, pp 1717–1724. https://doi.org/10.1109/CVPR.2014.222
Canny J (1986) A computational approach to edge detection. In IEEE Trans Pattern Anal Mach Intell PAMI-8(6):679–698. https://doi.org/10.1109/TPAMI.1986.4767851
Kanopoulos N, Vasanthavada N, Baker RL (1988) Design of an image edge detection filter using the Sobel operator. In IEEE J Solid-State Circ 23(2):358–367. https://doi.org/10.1109/4.996
Mehrotra R, Namuduri KR, Ranganathan N (1992) Gabor filter-based edge detection. Pattern Recogn 25(12):1479–1494. https://doi.org/10.1016/0031-3203(92)90121-X
Lowe DG (2004) Distinctive Image Features from scale-invariant keypoints. Int J Comput Vision 60(2):91–110. https://doi.org/10.1023/B:VISI.0000029664.99615.94
H. Bay, T. Tuytelaars, L. Van Gool (2006) “SURF: Speeded Up Robust Features,” In: A. Leonardis, H. Bischof, A. Pinz (eds) Computer Vision – ECCV 2006, ECCV 2006, Lecture Notes in Computer Science, vol. 3951. Springer, Berlin, Heidelberg doi: https://doi.org/10.1007/11744023_32
Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), San Diego, 1, pp. 886–893. https://doi.org/10.1109/CVPR.2005.177
Ojala T, Pietikainen M, Harwood D (1994) Performance evaluation of texture measures with classification based on Kullback discrimination of distributions. Proceedings of 12th International Conference on Pattern Recognition, Jerusalem, Israel 1:582–585. https://doi.org/10.1109/ICPR.1994.576366
Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. In Proceedings of the IEEE 86(11):2278-2324. https://doi.org/10.1109/5.726791
Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. Commun ACM 60(6):84–90. https://doi.org/10.1145/3065386
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:14091556
Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Ribinovich A (2015) Going Deeper with Convolution. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2015, pp 1–9, Boston
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016, Las Vegas, pp 770–778
Szegedy C, Vanhoucke V, Ioffe S et al (2016) Rethinking the inception architecture for computer vision. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016, Las Vegas, pp 2818–2826
Szegedy C, Ioffe S, Vanhoucke V, Alexander AA (2017) Inception-v4, inception-ResNet and the impact of residual connections on learning. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI'17), pp 4278–4284, AAAI Press
Chollet F (2017) Xception: Deep learning with depthwise separable convolutions. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, pp 1800–1807. https://doi.org/10.1109/CVPR.2017.195
Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications, arXiv preprint arXiv:1704.04861
Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, pp 2261–2269. https://doi.org/10.1109/CVPR.2017.243
Zoph B, Vasudevan V, Shlens J, Le QV (2018) Learning transferable architectures for scalable image recognition. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, pp 8697–8710. https://doi.org/10.1109/CVPR.2018.00907
Hu J, Shen L, Sun G (2018) Squeeze-and-Excitation Networks. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, pp 7132–7141. https://doi.org/10.1109/CVPR.2018.00745
Deng J, Dong W, Socher R, Li L, Li K, Fei-Fei L (2009) ImageNet: A large-scale hierarchical image database. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR-2009), Miami, pp 248–255. https://doi.org/10.1109/CVPR.2009.5206848
Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, pp 3431–3440. https://doi.org/10.1109/CVPR.2015.7298965
Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells W, Frangi A (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science, vol 9351. Springer, Cham, pp 234–241. https://doi.org/10.1007/978-3-319-24574-428
Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In Advances in NIPS, pp 2672–80
Bahdanau D, Cho K, Bengio Y (2014) Neural machine translation by jointly learning to align and translate. arXiv reprint arXiv:1409.0473
Radford A, Metz L, Chintala S (2015) Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv reprint arXiv:1511.06434
Tajbakhsh N, Shin JY, Gurudu SR, Hurst RT, Kendall CB, Gotway MB, Liang J (2016) Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE Trans Med Imaging 35(5):1299–1312. https://doi.org/10.1109/TMI.2016.2535302
Erhan D, Bengio Y, Courville A, Manzagol P-A, Vincent P, Bengio S (2010) Why does unsupervised pre-training help deep learning? J Mach Lear Res 11(3/1/2010):625–660
Pratt H, Coenen F, Broadbent DM, Harding SP, Zheng Y (2016) Convolutional neural networks for diabetic retinopathy. Proc Comput Sci 90:200–205. https://doi.org/10.1016/j.procs.2016.07.014
Doshi D, Shenoy A, Sidhpura D, Gharpure P (2016) Diabetic retinopathy detection using deep convolutional neural networks. 2016 International Conference on Computing, Analytics and Security Trends (CAST), Pune, pp 261–266. https://doi.org/10.1109/CAST.2016.7914977
Vo HH, Verma A (2016) New deep neural nets for fine-grained diabetic retinopathy recognition on hybrid color space. 2016 IEEE International Symposium on Multimedia (ISM), San Jose, pp 209–215. https://doi.org/10.1109/ISM.2016.0049
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 Ophthalmology 316(22):2402–2410. https://doi.org/10.1001/jama.2016.17216
Abràmoff 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):5200–5206. https://doi.org/10.1167/iovs.16-19964
Colas E, Besse A, Orgogozo A, Schmauch B, Meric N, Besse E (2016) Deep learning approach for diabetic retinopathy screening. Acta Ophthalmol 94(S256). https://doi.org/10.1111/j.1755-3768.2016.0635
Costa P, Campilho A (2017) Convolutional bag of words for diabetic retinopathy detection from eye fundus images. IPSJ Trans Comput Vis Appl 9:10. https://doi.org/10.1186/s41074-017-0023-6
Sivic J, Zisserman A (2003) Video Google: a text retrieval approach to object matching in videos. In Proceedings of Ninth IEEE International Conference on Computer Vision, Nice, France, vol 2, pp 1470-1477.https://doi.org/10.1109/ICCV.2003.1238663
Pires R, Jelinek HF, Wainer J, Valle E, Rocha A (2014) Advancing bag-of-visual-words representations for lesion classification in retinal images. PLoS ONE 9(6):e96814. https://doi.org/10.1371/journal.pone.0096814
Gargeya R, Leng T (2017) Automated identification of diabetic retinopathy using deep learning. Ophthalmology 124(7):962–969. https://doi.org/10.1016/j.ophtha.2017.02.008
Wang Z, Yin Y, Shi J, Fang W, Li H, Wang X (2017) Zoom-in-Net: Deep mining lesions for diabetic retinopathy detection. In: Descoteaux M, Maier-Hein L, Franz A, Jannin P, Collins D, Duchesne S (eds) Medical Image Computing and Computer Assisted Intervention − MICCAI 2017. MICCAI 2017. Lecture Notes in Computer Science, vol 10435. Springer, Cham, pp 267–275. https://doi.org/10.1007/978-3-319-66179-7_31
Quellec G, Charriére K, Boudi Y, Cochener B, Lamard M (2017) Deep image mining for diabetic retinopathy screening. Med Image Anal 39:178–193. https://doi.org/10.1016/j.media.2017.04.012
Antony M, Brüggeman S (2015) Team o_O Solution. https://www.kaggle.com/c/diabetic-retinopathy-detection/discussion/15617
Kauppi T, Kalesnykiene V, Kämäräinen J, Lensu L, Sorri I, Raninen A, Voutilainen R, Uusitalo H, Kälviäinen H, Pietilä J (2007) The DIARETDB1 Diabetic retinopathy database and evaluation protocol. In British Machine Vision Conference (BMVC), pp 1–10
Yang Y, Li T, Li W, Wu H, Fan W, Zhang W (2017) Lesion detection and grading of diabetic retinopathy via two-stages deep convolutional neural networks. In: Descoteaux M, Maier-Hein L, Franz A, Jannin P, Collins D, Duchesne S (eds) Medical image computing and computer assisted intervention − MICCAI 2017, MICCAI 2017, Lecture Notes in Computer Science, vol 10435. Springer, Cham, pp 533–540. https://doi.org/10.1007/978-3-319-66179-7_61
Wang Z, Yang J (2017) Diabetic retinopathy detection via deep convolutional networks for discriminative localization and visual explanation. arXiv preprint arXiv. 1703.10757.2017
Kanungo YS, Srinivasan B, Choudhary S (2017) Detecting diabetic retinopathy using deep learning. 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT). Bangalore, India, pp 801–804. https://doi.org/10.1109/RTEICT.2017.8256708
Gondal WM, Köhler JM, Grzeszick R, Fink GA, Hirsch M (2017)Weakly-supervised localization of diabetic retinopathy lesions in retinal fundus images. The IEEE International Conference on Image Processing (ICIP-2017), pp 2069–73. https://doi.org/10.1109/ICIP.2017.8296646
Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A (2016) Learning deep features for discriminative localization. In Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR’16), Las Vegas, pp 2921–2929. https://doi.org/10.1109/CVPR.2016.319
Ting DS, Cheung CY-L, Lim G, Tan GS, Quang ND, Gan A, Hamzah H, Garcia-Franco R, Yeo IYS, Lee SY et al (2017) Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. JAMA Ophthalmology 318(22):2211–2223. https://doi.org/10.1001/jama.2017.18152
García G, Gallardo J, Mauricio A, López J, Del Carpio C (2017) Detection of diabetic retinopathy based on a convolutional neural network using retinal fundus images. In: Lintas A, Rovetta S, Verschure P, Villa A (eds) Artificial Neural Networks and Machine Learning – ICANN 2017, Lecture Notes in Computer Science, vol 10614. Springer, Cham, pp 635–642. https://doi.org/10.1007/978-3-319-68612-7_72
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. 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Shanghai, pp 1–11. https://doi.org/10.1109/CISP-BMEI.2017.8301998
Wan S, Liang Y, Zhang Y (2018) Deep convolutional neural networks for diabetic retinopathy detection by image classification. Comput Electr Eng 72:274–282. https://doi.org/10.1016/j.compeleceng.2018.07.042
Buades A, Coll B, Morel J (2005) A non-local algorithm for image denoising. In Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), San Diego, vol 2, pp 60–65. https://doi.org/10.1109/CVPR.2005.38
Mansour RF (2018) Deep-learning-based automatic computer-aided diagnosis system for diabetic retinopathy. Biomed Eng Lett 8(1):41–57. https://doi.org/10.1007/s13534-017-0047-y
Antal B, Hajdu A (2012) An ensemble-based system for microaneurysm detection and diabetic retinopathy grading. IEEE Trans Biomed Eng 59(6):1720–1726. https://doi.org/10.1109/TBME.2012.2193126
Walter T, Klein JC (2002) Automatic detection of microaneurysms in color fundus images of the human retina by means of the bounding box closing. In: Colosimo A, Sirabella P, Giuliani A (eds) Medical Data Analysis: Third International Symposium (ISMDA 2002), Lecture Notes in Computer Science, vol 2526. Springer, Heidelberg, pp 210–220. https://doi.org/10.1007/3-540-36104-9_23
Zuiderveld K (1994) Contrast limited adaptive histogram equalization. Graphics Gems 4:474–485
Youssif A, Ghalwash AZ, Ghoneim AS (2006) Comparative study of contrast enhancement and illumination equalization methods for retinal vasculature segmentation. In Proceedings of Third Cairo International Biomedical Engineering Conference (CIBEC’06), pp 21–24
Stauffer C, Grimson WEL (1999) Adaptive background mixture models for real-time tracking. In Proceedings of 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CAT. No PR00149) 2:246-252. Fort Collins, CO, USA. https://doi.org/10.1109/CVPR.1999.784637
Criminisi A, Perez P, Toyama K (2003) Object removal by exemplar based inpainting. In Proceedings of 2003 IEEE Conference on Computer Vision and Pattern Recognition (CVPR’03). 2 Madison, WI, USA, pp 721–728. https://doi.org/10.1109/CVPR.2003.1211538
Chen Y-W, Wu T-Y, Wong W-H, Lee C-Y (2018) Diabetic retinopathy detection based on deep convolutional neural networks. 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AB, pp 1030–1034.https://doi.org/10.1109/ICASSP.2018.8461427
Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66. https://doi.org/10.1109/TSMC.1979.4310076
Polesel A, Ramponi G, Mathews VJ (2000) Image enhancement via adaptive unsharp masking. IEEE Trans Image Process 9(3):505–510. https://doi.org/10.1109/83.826787
Lam C, Yi D, Guo M, Lindsey T (2018) Automated detection of diabetic retinopathy using deep learning. In Proceedings of AMIA Joint Summits on Translational Science 2017:147–155
Lin Z, Guo R, Wang Y, Wu B, Chen T, Wang W, Chen DZ (2018) A framework for identifying diabetic retinopathy based on anti-noise detection and attention-based fusion. In: Frangi A, Schnabel J, Davatzikos C, Alberola-López C, Fichtinger G (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, MICCAI 2018, Lecture Notes in Computer Science, vol 11071. Springer, Cham, pp 74–82. https://doi.org/10.1007/978-3-030-00934-2_9
Wen Y, Zhang K, Li Z, Qiao Y (2016) A discriminative feature learning approach for deep face recognition. In: Leibe B, Matas J, Sebe N, Welling M (eds) Computer Vision – ECCV 2016, ECCV 2016, Lecture Notes in Computer Science, vol 9911. Springer, Cham, pp 499–515. https://doi.org/10.1007/978-3-319-46478-731
Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C, Berg AC (2016) SSD: single shot multibox detector. In: Leibe B, Matas J, Sebe N, Welling M (eds) ECCV 2016, ECCV 2016, Lecture Notes in Computer Science, vol 9905. Springer, Cham, pp 21–37. https://doi.org/10.1007/978-3-319-46448-0_2
Junjun P, Zhifan Y, Dong S, Hong Q (2018) Diabetic retinopathy detection based on deep convolutional neural networks for localization of discriminative regions. 2018 International Conference on Virtual Reality and Visualization (ICVRV), Qingdao, China, pp 46–52. https://doi.org/10.1109/ICVRV.2018.00016
Graham B (2015) Kaggle diabetic retinopathy detection competition report. University of Warwick
Orlando JI, Prokofyeva E, Fresno MD, Blaschko MB (2018) An ensemble deep learning based approach for red lesion detection in fundus images. Comput Methods Programs Biomed 153:115–127. https://doi.org/10.1016/j.cmpb.2017.10.017
Kori A, Chennamsetty SS, Mohammed Safwan KP, Varghese A (2018) Ensemble of convolutional neural networks for automatic grading of diabetic retinopathy and macular edema. arXiv reprint arXiv: abs/1809.04228
Mateen M, Wen J, Song Nasrullah S, Huang Z (2019) Fundus Image Classification Using VGG-19 Architecture with PCA and SVD. Symmetry 11(1):1. https://doi.org/10.3390/sym11010001
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. 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany, pp 2699–2702. https://doi.org/10.1109/EMBC.2019.8857073
Lazar I, Hajdu A (2013) Retinal microaneurysm detection through local rotating cross-section profile analysis. In IEEE Trans Med Imaging 32(2):400–407. https://doi.org/10.1109/TMI.2012.2228665
Walter T, Massin P, Erginay A, Ordonez R, Jeulin C, Klein JC (2007) Automatic detection of microaneurysms in color fundus images. Med Image Anal 11(6):555–566. https://doi.org/10.1016/j.media.2007.05.001
Zhang B, Wu X, You J, Li Q, Karray F (2010) Detection of microaneurysms using multi-scale correlation coefficients. Pattern Recogn 43(6):2237–2248. https://doi.org/10.1016/j.patcog.2009.12.017
Finlayson GD, Schiele B, Crowley JL (1998) Comprehensive colour image normalization. In: Burkhardt H, Neumann B (eds) Computer Vision – ECCV’98, ECCV 1998, Lecture Notes in Computer Science, vol 1406. Springer, Heidelberg, pp 475–490. https://doi.org/10.1007/BFb0055685
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. In IEEE Trans Med Imaging 21(10):1236–1243. https://doi.org/10.1109/TMI.2002.806290
Welfer D, Scharcanski J, Marinho DR (2010) A coarse-to-fine strategy for automatically detecting exudates in color eye fundus images. Comput Med Imaging Graph 34(3):228–235. https://doi.org/10.1016/j.compmedimag.2009.10.001
Ahmad M, Kasukurthi N, Pande H (2019) Deep learning for weak supervision of diabetic retinopathy abnormalities. 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), Venice, Italy, pp 573–577. https://doi.org/10.1109/ISBI.2019.8759417
Zhou Y, He X, Huang L, Liu L, Zhu F, Cui S, Shao L (2019) Collaborative learning of semi-supervised segmentation and classification for medical images. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR’19), Long Beach, CA, USA, pp 2074–2083. https://doi.org/10.1109/CVPR.2019.00218
Lin T, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. IEEE International Conference on Computer Vision (ICCV), Venice, pp 2999–3007. https://doi.org/10.1109/ICCV.2017.324
Li X, Hu X, Yu L, Zhu L, Fu C, Heng P (2020) CANet: Cross-disease attention network for joint diabetic retinopathy and diabetic macular edema grading. In IEEE Trans Med Imaging 39(5):1483–1493. https://doi.org/10.1109/TMI.2019.2951844
Saxena G, Verma DK, Paraye A, Rajan A, Rawat A (2020) Improved and robust deep learning agent for preliminary detection of diabetic retinopathy using public datasets. Intell-Based Med 3(4). https://doi.org/10.1016/j.ibmed.2020.100022
Zhang Z (2020) Deep-learning-based early detection of diabetic retinopathy on fundus photography using efficientNet.” In Proceedings of the 2020 the 4th International Conference on Innovation in Artificial Intelligence (ICIAI 2020). Association for Computing Machinery, New York, pp 70–74. https://doi.org/10.1145/3390557.3394303
Tan M, Le Q (2019) EfficientNet: Rethinking model scaling for convolutional neural networks. In Proceedings of the 36th International Conference on Machine Learning, In Proceedings of Machine Learning Research
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
Dekhil O, Naglah A, Shaban M, Ghazal M, Taher F, Elbaz A (2019) Deep learning based method for computer aided diagnosis of diabetic retinopathy. In Proceedings of the IST 2019—IEEE International Conference on Imaging Systems and Techniques, Abu Dhabi, United Arab Emirates; pp. 1–4
Kassani SH, Kassani PH, Khazaeinezhad R, Wesolowski MJ, Schneider KA, Deters R (2019) Diabetic retinopathy classification using a modified Xception architecture. In Proceedings of the 2019 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), Ajman, United Arab Emirates, pp 1–6
He A, Li T, Li N, Wang K, Fu H (2020) CABNet: Category attention block for imbalanced diabetic retinopathy grading. IEEE Trans Med Imaging 40:143–153
Bodapati JD, Shaik NS, Naralasetti V (2021) Composite deep neural network with gated-attention mechanism for diabetic retinopathy severity classification. J Ambient Intell Humaniz Comput 12:9825–9839. https://doi.org/10.1007/s12652-020-02727-z
Alyoubi WL, Abulkhair MF, Shalash WM (2021) Diabetic retinopathy fundus image classification and lesions localization system using deep learning. Sensors 21(11):3704. https://doi.org/10.3390/s21113704
Deepa V, Kumar CS, Cherian T (2022) Ensemble of multi-stage deep convolutional neural networks for automated grading of diabetic retinopathy using image patches. J King Saud Univ - Comput Inf Sci 34(8, Part B):6255–6265. https://doi.org/10.1016/j.jksuci.2021.05.009. (ISSN 1319-1578)
Author information
Authors and Affiliations
Contributions
Nilarun Mukherjee and Souvik Sengupta wrote the main manuscript text, tables and prepared the figures. All authors reviewed and approved the final manuscript.
Corresponding author
Ethics declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Human/Animal rights
This article does not contain any studies with human or animal subjects performed by the any of the authors.
Conflict of interest
Nilarun Mukherjee and Souvik Sengupta declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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.
About this article
Cite this article
Mukherjee, N., Sengupta, S. Application of deep learning approaches for classification of diabetic retinopathy stages from fundus retinal images: a survey. Multimed Tools Appl 83, 43115–43175 (2024). https://doi.org/10.1007/s11042-023-17254-0
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-023-17254-0