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Developments in the detection of diabetic retinopathy: a state-of-the-art review of computer-aided diagnosis and machine learning methods

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Abstract

The exponential increase in the number of diabetics around the world has led to an equally large increase in the number of diabetic retinopathy (DR) cases which is one of the major complications caused by diabetes. Left unattended, DR worsens the vision and would lead to partial or complete blindness. As the number of diabetics continue to increase exponentially in the coming years, the number of qualified ophthalmologists need to increase in tandem in order to meet the demand for screening of the growing number of diabetic patients. This makes it pertinent to develop ways to automate the detection process of DR. A computer aided diagnosis system has the potential to significantly reduce the burden currently placed on the ophthalmologists. Hence, this review paper is presented with the aim of summarizing, classifying, and analyzing all the recent development on automated DR detection using fundus images from 2015 up to this date. Such work offers an unprecedentedly thorough review of all the recent works on DR, which will potentially increase the understanding of all the recent studies on automated DR detection, particularly on those that deploys machine learning algorithms. Firstly, in this paper, a comprehensive state-of-the-art review of the methods that have been introduced in the detection of DR is presented, with a focus on machine learning models such as convolutional neural networks (CNN) and artificial neural networks (ANN) and various hybrid models. Each AI will then be classified according to its type (e.g. CNN, ANN, SVM), its specific task(s) in performing DR detection. In particular, the models that deploy CNN will be further analyzed and classified according to some important properties of the respective CNN architectures of each model. A total of 150 research articles related to the aforementioned areas that were published in the recent 5 years have been utilized in this review to provide a comprehensive overview of the latest developments in the detection of DR.

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References

  • Abbas Q, Fondon I, Sarmiento A, Jiménez S, Alemany P (2017) Automatic recognition of severity level for diagnosis of diabetic retinopathy using deep visual features. Med Biol Eng Comput 55:1959–1974

    Article  Google Scholar 

  • Abdelsalam MM (2020) Effective blood vessels reconstruction methodology for early detection and classification of diabetic retinopathy using OCTA images by artificial neural network. Inf Med Unlocked 20:100390

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Aiello LP, Avery RL, Arrigg PG, Keyt BA, Jampel HD, Shah ST, Pasquale LR, Thieme H, Iwamoto MA, Park JE, Nguyen HV, Aiello LM, Ferrara N, King GL (1994) Vascular endothelial growth factor in ocular fluid of patients with diabetic retinopathy and other retinal disorders. N Engl J Med 331(22):1480–1487

    Article  Google Scholar 

  • Akyol K, Bayir Ş, Şen B (2016) Detection of hard exudates in diabetic retinopathy retinal images by utilizing visual dictionary and classifier approaches. Mugla J Sci Technol 2(1):1–6

    Article  Google Scholar 

  • Al-Hazaimeh OM, Nahar KMO, Al-Naami B, Gharaibeh N (2018) An effective image processing method for detection of diabetic retinopathy diseases from retinal fundus images. Int J Signal Imaging Syst Eng 11(4):206–216

    Article  Google Scholar 

  • Ali A, Qadri S, Mashwani WK, Kumam W, Kumam P, Naeem S, Goktas A, Jamal F, Chesneau C, Anam S, Sulaiman M (2020) Machine learning based automated segmentation and hybrid feature analysis for diabetic retinopathy classification using fundus image. Entropy 22(5):567

    Article  Google Scholar 

  • Al-Jarrah MA, Shatnawi H (2017) Non-proliferative diabetic retinopathy symptoms detection and classification using neural network. J Med Eng Technol 41(6):498–505

    Article  Google Scholar 

  • Alyoubi WL, Shalash WM, Abulkhair MF (2020) Diabetic retinopathy detection through deep learning techniques: a review. Inf Med Unlocked 20:100377

    Article  Google Scholar 

  • Amin J, Sharif M, Yasmin M (2016) A review on recent developments for detection of diabetic retinopathy. Scientifica 2016:6838976

    Article  Google Scholar 

  • Antonetti DA, Barber AJ, Bronson SK, Freeman WM, Gardner TW, Jefferson LS, Kester M, Kimball SR, Krady JK, LaNoue KF, Norbury CC, Quinn PG, Sandirasegarane L, Simpson IA, JDRF Diabetic Retinopathy Center Group (2016) Diabetic retinopathy: seeing beyond glucose-induced microvascular disease. Diabetes 55(9):2401–2411

    Article  Google Scholar 

  • Ayhan MS, Kühlewein L, Aliyeva G, Inhoffen W, Ziemssen F, Berens P (2020) Expert-validated estimation of diagnostic uncertainty for deep neural networks in diabetic retinopathy detection. Med Image Anal 64:101724

    Article  Google Scholar 

  • Banerjee S, Kayal D (2016) Detection of hard exudates using mean shift and normalized cut method. Biocybern Biomed Eng 36(4):679–685

    Article  Google Scholar 

  • Barkana BD, Saricicek I, Yildirim B (2017) Performance analysis of descriptive statistical features in retinal vessel segmentation via fuzzy logic, ANN, SVM, and classifier fusion. Knowl Based Syst 118:165–176

    Article  Google Scholar 

  • Bellemo V, Lim ZW, Lim G, Nguyen GD, Xie Y, Yip MYT, Hamzah H, Ho J, Lee XQ, Hsu W, Lee ML, Musonda L, Chandran M, Chipalo-Mutati G, Muma M, Tan GSW, Sivaprasad S, Menon G, Wong TY, Ting DSW (2019) Artificial intelligence using deep learning to screen for referable and vision-threatening diabetic retinopathy in Africa: a clinical validation study. Lancet Digital Health 1(1):e35–e44

    Article  Google Scholar 

  • Bhardwaj C, Jain S, Sood M (2021) Hierarchical severity grade classification of non-proliferative diabetic retinopathy. J Ambient Intell Humaniz Comput 12:2649–2670

    Article  Google Scholar 

  • Brown JM, Campbell JP, Beers A, Chang K, Ostmo S, Chan RVP, Dy J, Erdogmus D, Ioannidis S, Kalpathy-Cramer J, Chiang MF (2018) Automated diagnosis of plus disease in retinopathy of prematurity using deep convolutional neural networks. JAMA Ophthalmol 136(7):803–810

    Article  Google Scholar 

  • Cano J, O’neill WD, Penn RD, Blair NP, Kashani AH, Ameri H, Kaloostian CL, Shahidi M (2020) Classification of advanced and early stages of diabetic retinopathy from non-diabetic subjects by an ordinary least squares modeling method applied to OCTA images. Biomed Opt Express 11(8):4666–4678

    Article  Google Scholar 

  • Cao W, Czarnek N, Shan J, Li L (2018) Microaneurysm detection using principal component analysis and machine learning methods. IEEE Trans Nanobiosci 17(3):191–198

    Article  Google Scholar 

  • Carlo TE, Chin AT, Filho MAB, Adhi M, Branchini L, Salz DA, Baumal CR, Crawford C, Reichel E, Witkin AJ, Duker JS, Waheed NK (2015) Detection of microvascular changes in eyes of patients with diabetes but not clinical diabetic retinopathy using optical coherence tomography angiography. Retina 35(11):2364–2370

    Article  Google Scholar 

  • Chaki J, Ganesh ST, Cidham SK, Theertan SA (2020) Machine learning and artificial intelligence based diabetes mellitus detection and self-management: a systematic review. J King Saud Univ Comput Inf Sci. https://doi.org/10.1016/j.jksuci.2020.06.013

    Article  Google Scholar 

  • Chowdhury AR, Chatterjee T, Banerjee S (2019) A random forest classifier-based approach in the detection of abnormalities in the retina. Med Biol Eng Comput 57:193–203

    Article  Google Scholar 

  • Colomer A, Igual J, Naranjo V (2020) Detection of early signs of diabetic retinopathy based on textural and morphological information in fundus images. Sensors 20(4):1005

    Article  Google Scholar 

  • Das V, Puhan NB (2017) Tsallis entropy and sparse reconstructive dictionary learning for exudate detection in diabetic retinopathy. J Med Imaging 4(2):024002

    Article  Google Scholar 

  • Derwin DJ, Selvi ST, Singh OJ, Shan BP (2020a) A novel automated system of discriminating microaneurysms in fundus images. Biomed Signal Process Control 58:101839

    Article  Google Scholar 

  • Derwin DJ, Selvi ST, Singh OJ (2020b) Secondary observer system for detection of microaneurysms in fundus images using texture descriptors. J Digit Imaging 33:159–167

    Article  Google Scholar 

  • Duh EJ, Sun JK, Stitt AW (2017) Diabetic retinopathy: current understanding, mechanisms, and treatment strategies. JCI Insight 2(14):e93751

    Article  Google Scholar 

  • Dutta MK, Parthasarathi M, Ganguly S, Ganguly S, Srivastava K (2015) An efficient image processing based technique for comprehensive detection and grading of nonproliferative diabetic retinopathy from fundus images. Comput Methods Biomech Biomed Eng 5(3):195–207

    Google Scholar 

  • Eftekhari N, Pourreza HR, Masoudi M, Ghiasi-Shirazi K, Sae E (2019) Microaneurysm detection in fundus images using a two-step convolutional neural network. Biomed Eng Online 18:67

    Article  Google Scholar 

  • Gadekallu TR, Khare N, Bhattacharya S, Singh S, Maddikunta PKR, Srivastava G (2020a) Deep neural networks to predict diabetic retinopathy. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-020-01963-7

    Article  Google Scholar 

  • Gadekallu TR, Khare N, Bhattacharya S, Singh S, Maddikunta PKR, Ra IH, Alazab M (2020b) Early detection of diabetic retinopathy using PCA-Firefly based deep learning model. Electronics 9(2):274

    Article  Google Scholar 

  • Gao Z, Li J, Guo J, Chen Y, Yi Z, Zhong J (2018) Diagnosis of diabetic retinopathy using deep neural networks. IEEE Access 7:3360–3370

    Article  Google Scholar 

  • Gayathri S, Varun PG, Palanisamy P (2020) A lightweight CNN for diabetic retinopathy classification from fundus images. Biomed Signal Process Control 62:102115

    Article  Google Scholar 

  • Genuth S, Sun W, Cleary P, Gao X, Sell DR, Lachin J, Monnier VM (2015) Skin advanced glycation end products glucosepane and methylglyoxal hydroimidazolone are independently associated with long-term microvascular complication progression of type 1 diabetes. Diabetes 64(1):266–278

    Article  Google Scholar 

  • Gharaibeh NY (2016) A novel approach for detection of microaneurysms in diabetic retinopathy disease from retinal fundus images. Comput Inf Sci 10(1):1–15

    Google Scholar 

  • Gharaibeh NY, Alshorman AA (2016) An effective diagnosis of diabetic retinopathy with aid of soft computing approaches. J Power Energy Eng 10(8):474–485

    Google Scholar 

  • Gonzalez-Gonzalo C, Sanchez-Gutierrez V, Hernandez-Martınez P, Contreras I, Lechanteur YT, Domanian A, Ginneken BV, Sanchez CC (2020) Evaluation of a deep learning system for the joint automated detection of diabetic retinopathy and age-related macular degeneration. Acta Ophthalmol 98(4):368–377

    Article  Google Scholar 

  • Gross JG, Glassman AR, Jampol LM, Inusah S, Aiello LP, Antoszyk AN, Baker CW, Berger BB, Bressler NM, Browning D, Elman MJ, Ferris FL 3rd, Friedman SM, Marcus DM, Melia M, Stockdale CR, Sun JK, Beck RW (2015) Panretinal photocoagulation vs intravitreous ranibizumab for proliferative diabetic retinopathy: a randomized clinical trial. JAMA 314(20):2137–2146

    Article  Google Scholar 

  • Grzybowski A, Brona P, Lim G, Ruamviboonsuk P, Tan GSW, Abramoff M, Ting DSW (2019) Artificial intelligence for diabetic retinopathy screening: a review. Eye 34(3):451–460

    Article  Google Scholar 

  • Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J, Kim R, Raman R, Nelson PC, Mega JL, Webster DR (2016) Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316(22):2402–2410

    Article  Google Scholar 

  • Gulshan V, Rajan RP, Widner K, Wu D, Wubbels P, Rhodes T, Whitehouse K, Coram M, Corrado G, Ramasamy K, Raman R, Peng L, Webster DR (2019) Performance of a deep-learning algorithm vs manual grading for detecting diabetic retinopathy in India. JAMA Ophthalmol 137(9):987–993

    Article  Google Scholar 

  • Guo YK, Hormel TT, Xiong HL, Wang BJ, Camino A, Wang J, Huang D, Hwang TS, Jia YL (2019) Development and validation of a deep learning algorithm for distinguishing the nonperfusion area from signal reduction artifacts on OCT angiography. Biomed Opt Express 10(7):3257–3268

    Article  Google Scholar 

  • Gupta G, Kulasekaran S, Ram K, Joshi N, Sivaprakasam M, Gandhi R (2017) Local characterization of neovascularization and identification of proliferative diabetic retinopathy in retinal fundus images. Comput Med Imaging Graph 55:124–132

    Article  Google Scholar 

  • He J, Cao T, Xu F, Wang S, Tao H, Wu T, Sun L, Chen J (2019) Artificial intelligence-based screening for diabetic retinopathy at community hospital. Eye 34(3):572–576

    Article  Google Scholar 

  • He K, Zhang X, Ren S, Sun J (2015) Deep residual learning for image recognition. In: Proceedings of the 2016 IEEE conference on computer vision and pattern recognition (CVPR), Las Vegas

  • Heisler M, Karst S, Lo J, Mammo Z, Yu T, Warner S, Maberley D, Beg MF, Navajas EV, Sarunic MV (2020) Ensemble deep learning for diabetic retinopathy detection using optical coherence tomography angiography. Transl vis Sci Technol 9(2):20

    Article  Google Scholar 

  • Hemanth DJ, Anitha J, Son LH, Mittal M (2018) Diabetic retinopathy diagnosis from retinal images using modified hopfield neural network. J Med Syst 42(12):247

    Article  Google Scholar 

  • Hemanth DJ, Deperlioglu O, Kose U (2019) An enhanced diabetic retinopathy detection and classification approach using deep convolutional neural network. Neural Comput Appl 32:707–721

    Article  Google Scholar 

  • Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the 2018 IEEE conference on computer vision and pattern recognition (CVPR), Salt Lake City, pp 7132–7141

  • Huang G, Liu Z, Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the 2017 IEEE conference on computer vision and pattern recognition (CVPR), Honolulu, pp 4700–4708

  • Ibrahim S, Chowriappa P, Dua S, Acharya UR, Noronha K, Bhandary S, Mugasa H (2015) Classification of diabetes maculopathy images using data-adaptive neuro-fuzzy inference classifier. Med Biol Eng Comput 53:1345–1360

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Islam MM, Yang HC, Poly TN, Jian WS, Li YC (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

    Article  Google Scholar 

  • Jadhav AS, Patil PB, Biradar S (2020) Computer-aided diabetic retinopathy diagnostic model using optimal thresholding merged with neural network. Int J Intell Comput Cybern 13(3):283–310

    Article  Google Scholar 

  • Javidi M, Pourreza HR, Harati A (2017) Vessel segmentation and microaneurysm detection using discriminative dictionary learning and sparse representation. Comput Methods Programs Biomed 139:93–108

    Article  Google Scholar 

  • Jebaseeli TJ, Durai CAD, Peter JD (2019a) Segmentation of retinal blood vessels from ophthalmologic diabetic retinopathy images. Comput Electr Eng 73:245–258

    Article  Google Scholar 

  • Jebaseeli TJ, Durai CAD, Peter JD (2019b) Retinal blood vessel segmentation from diabetic retinopathy images using tandem PCNN model and deep learning based SVMT. Optik 199:163328

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Kaur J, Mittal D (2018) A generalized method for the segmentation of exudates from pathological retinal fundus images. Biocybern Biomed Eng 38(1):27–53

    Article  Google Scholar 

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

    Google Scholar 

  • Le D, Alam M, Yao CK, Lim JI, Hsieh YT, Chan RVP, Toslak D, Yao X (2020) Transfer learning for automated OCTA detection of diabetic retinopathy. Transl vis Sci Technol 9(2):35

    Article  Google Scholar 

  • Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324

    Article  Google Scholar 

  • Lee CS, Tyring AJ, Wu Y, Xiao S, Rokem AS, DeRuyter NP, Zhang Q, Tufail A, Wang RK, Lee AY (2019) Generating retinal flow maps from structural optical coherence tomography with artificial intelligence. Sci Rep 9:5694

    Article  Google Scholar 

  • Li Q, Fan S, Chen C (2019a) An intelligent segmentation and diagnosis method for diabetic retinopathy based on improved U-NET network. J Med Syst 43:304

    Article  Google Scholar 

  • Li YH, Yeh NN, Chen SJ, Chung YC (2019b) Computer-assisted diagnosis for diabetic retinopathy based on fundus images using deep convolutional neural network. Mob Inf Syst 2019:6142839

    Google Scholar 

  • Liu YP, 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

    Article  Google Scholar 

  • Lu W, Tong Y, Yu Y, Xing Y, Chen C, Shen Y (2018) Applications of artificial intelligence in ophthalmology: general overview. J Ophthalmol 2018:527819

    Article  Google Scholar 

  • Luo YG, Pan J, Fan SS, Du ZY, Zhan GH (2020) Retinal image classification by self-supervised fuzzy clustering network. IEEE Access 8:92352–92362

    Google Scholar 

  • Mahendran G, Dhanasekaran R (2015a) Investigation of the severity level of diabetic retinopathy using supervised classifier algorithms. Comput Electr Eng 45:312–323

    Article  Google Scholar 

  • Mahendran G, Dhanasekaran R (2015b) Detection and localization of retinal exudates for diabetic retinopathy. J Biol Syst 23(2):195–212

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Mateen M, Wen J, Nasrullah N, Sun S, Hayat S (2020) Exudate detection for diabetic retinopathy using pretrained convolutional neural networks. Complexity 2020:5801870

    Article  Google Scholar 

  • Memari N, Ramli AR, Saripan MI, Mashohor S, Moghbel M (2019) Retinal blood vessel segmentation by using matched filtering and fuzzy c-means clustering with integrated level set method for diabetic retinopathy assessment. J Med Biol Eng 39:713–731

    Article  Google Scholar 

  • Mongan J, Moy L, Kahn CE (2020) Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers. Radiology 2:2

    Google Scholar 

  • Nagendran M, Chen Y, Lovejoy CA, Gordon AC, Komorowski M, Harvey H, Topol EJ, Ioannidis JPA, Collins GS, Maruthappu M (2020) Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies. BMJ 368:689

    Article  Google Scholar 

  • Nair AT, Muthuvel K (2019) Blood vessel segmentation and diabetic retinopathy recognition: an intelligent approach. Comput Methods Biomech Biomedi Eng 8(2):169–181

    Google Scholar 

  • Natarajan S, Jain A, Krishnan R, Rogye A, Sivaprasad S (2019) Diagnostic accuracy of community-based diabetic retinopathy screening with an offline artificial intelligence system on a smartphone. JAMA Ophthalmol 137(10):1182–1188

    Article  Google Scholar 

  • Nazir T, Irtaza A, Shabbir Z, Javed A, Akram U, Mahmood MT (2019) Diabetic retinopathy detection through novel tetragonal local OCTA patterns and extreme learning machines. Artif Intell Med 99:101695

    Article  Google Scholar 

  • Nilashi M, Samad S, Yadegaridehkordi E, Alizadeh A, Akbari E, Ibrahim O (2019) Early detection of diabetic retinopathy using ensemble learning approach. J Soft Comput Decis Support Syst 6(2):12–17

    Google Scholar 

  • Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Akl EA, Brennan SE, Chou R, Glanville J, Grimshaw JM, Hróbjartsson A, Lalu MM, Li T, Loder E, Mayo-Wilson E, McDonald S, McGuinness LA, Stewart LA, Thomas J, Tricco AC, Welch VA, Whiting P, Moher D (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372:n71. https://doi.org/10.1136/bmj.n71

    Article  Google Scholar 

  • Pao SI, Lin HZ, Chien KH, Tai MC, Chen JT, Lin GM (2020) Detection of diabetic retinopathy using bichannel convolutional neural network. J Ophthalmol 2020:9139713

    Article  Google Scholar 

  • Partovi M, Rasta SH, Javadzadeh A (2016) Automatic detection of retinal exudates in fundus images of diabetic retinopathy patients. J Anal Res Clin Med 4(2):104–109

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Pratheeba C, Singh NN (2019) A novel approach for detection of hard exudates using random forest classifier. J Med Syst 43(7):180

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Qiao L, Zhu Y, Zhou H (2020) Diabetic retinopathy detection using prognosis of microaneurysm and early diagnosis system for non-proliferative diabetic retinopathy based on deep learning algorithms. IEEE Access 8:104292–104302

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Qummar S, Khan FG, Shah S, Khan A, Shamshirband S, Rehman ZU, Khan IA, Jadoon W (2019) A deep learning ensemble approach for diabetic retinopathy detection. IEEE Access 7:150530–150539

    Article  Google Scholar 

  • Rajalakshmi R, Subashini R, Anjana RM, Mohan V (2018) Automated diabetic retinopathy detection in smartphone-based fundus photography using artificial intelligence. Eye 32(6):1138–1144

    Article  Google Scholar 

  • Raju M, Pagidimarri V, Barreto R, Kadam A, Kasivajjala V, Aswath A (2017) Development of a deep learning algorithm for automatic diagnosis of diabetic retinopathy. Stud Health Technol Inf 245:559–563

    Google Scholar 

  • Ramachandran N, Chiong HS, Sime MJ, Wilson GA (2018) Diabetic retinopathy screening using deep neural network. Clin Exp Ophthalmol 46(4):412–416

    Article  Google Scholar 

  • Raman R, Srinivasan S, Virmani S, Sivaprasad S, Rao C, Rajalakshmi R (2019) Fundus photograph-based deep learning algorithms in detecting diabetic retinopathy. Eye 33:97–109

    Article  Google Scholar 

  • Rami HE, Barham R, Sun JK, Silva PS (2017) Evidence-based treatment of diabetic retinopathy. Semin Ophthalmol 32(1):67–74

    Article  Google Scholar 

  • Randive SN, Rahulkar AD, Senapati RK (2018) LVP extraction and triplet-based segmentation for diabetic retinopathy recognition. Evol Intel 11(1–2):117–129

    Article  Google Scholar 

  • Rogers TW, Gonzalez-Bueno J, Franco RG, Star EL, Marín DM, Vassallo J, Lansingh VC, Trikha S, Jaccard N (2021) Evaluation of an AI system for the detection of diabetic retinopathy from images captured with a handheld portable fundus camera: the MAILOR AI study. Eye 35:632–638

    Article  Google Scholar 

  • Sabanayagam C, Banu R, Chee ML, Lee R, Wang YX, Tan G, Jonas JB, Lamoureux EL, Cheng CY, Klein BEK, Mitchell P, Klein R, Cheung CMG, Wong TY (2018) Incidence and progression of diabetic retinopathy: a systematic review. Lancet Diabetes Endocrinol 7(2):140–149

    Article  Google Scholar 

  • Sahlsten J, Jaskari J, Kivinen J, Turunen L, Jaanio E, Hietala K, Kaski K (2019) Deep learning fundus image analysis for diabetic retinopathy and macular edema grading. Sci Rep 9:10750

    Article  Google Scholar 

  • Saleh E, Błaszczyński J, Moreno A, Valls A, Romero-Aroca P, Riva-Fernández SDL, Słowiński R (2018) Learning ensemble classifiers for diabetic retinopathy assessment. Artif Intell Med 85:50–63

    Article  Google Scholar 

  • Samanta A, Saha A, Satapathy SC, Fernandes SL, Zhang YD (2020) Automated detection of diabetic retinopathy using convolutional neural networks on a small dataset. Pattern Recogn Lett 135:293–298

    Article  Google Scholar 

  • Sandhu HS, Eladawi N, Elmogy M, Keynton R, Helmy O, Schaal S, El-Baz A (2018a) Automated diabetic retinopathy detection using optical coherence tomography angiography: a pilot study. Br J Ophthalmol 102(11):1564–1569

    Article  Google Scholar 

  • Sandhu HS, Eltanboly A, Shalaby A, Keynton RS, Schaal S, El-Baz A (2018b) Automated diagnosis and grading of diabetic retinopathy using optical coherence tomography. Invest Ophthalmol vis Sci 59:3155–3160

    Article  Google Scholar 

  • Sandhu HS, Elmogy M, Sharafeldeen AT, Elsharkawy M, El-Adawy N, Eltanboly A, Shalaby A, Keynton R, El-Baz A (2020) Automated diagnosis of diabetic retinopathy using clinical biomarkers, optical coherence tomography, and optical coherence tomography angiography. Am J Ophthalmol 216:201–206

    Article  Google Scholar 

  • Seth S, Agarwal B (2018) A hybrid deep learning model for detecting diabetic retinopathy. J Stat Manag Syst 21(4):569–574

    Google Scholar 

  • Shaban M, Ogur Z, Mahmoud A, Switala A, Shalaby A, Khalifeh HA, Ghazal M, Fraiwan L, Giridharan G, Sandhu H, El-Baz AS (2020) A convolutional neural network for the screening and staging of diabetic retinopathy. PLoS ONE 15(6):e0233514

    Article  Google Scholar 

  • Shah P, Mishra DK, Shanmugam MP, Doshi B, Jayaraj H, Ramanjulu R (2020) Validation of deep convolutional neural network-based algorithm for detection of diabetic retinopathy: artificial intelligence versus clinician for screening. Indian J Ophthalmol 68(2):398–405

    Article  Google Scholar 

  • Shankar K, Sait ARW, Guptac D, Lakshmanaprabu SK, Khanna A, Pandey HM (2020a) Automated detection and classification of fundus diabetic retinopathy images using synergic deep learning model. Pattern Recogn Lett 133:210–216

    Article  Google Scholar 

  • Shankar K, Zhang Y, Liu Y, Wu L, Chen CH (2020b) Hyperparameter tuning deep learning for diabetic retinopathy fundus image classification. IEEE Access 8:118164–118173

    Article  Google Scholar 

  • Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In Proceedings of the 3rd international conference on learning representations (ICLR) 2015, San Diego

  • Son J, Shin JY, Kim HD, Jung KH, Park KH, Park SJ (2019) Development and validation of deep learning models for screening multiple abnormal findings in retinal fundus images. Ophthalmology 127(1):85–94

    Article  Google Scholar 

  • Sorour O, Arya M, Waheed N (2018) New findings and challenges in OCT angiography for diabetic retinopathy. Ann Eye Sci 3(8):44

    Article  Google Scholar 

  • Srinivasan PP, Kim LA, Mettu PS, Cousins SW, Comer GM, Izatt JA, Farsiu S (2014) Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images. Biomed Opt Express 5(10):3568–3577

    Article  Google Scholar 

  • Srivastava V, Purwar RK (2020) Classification of eye-fundus images with diabetic retinopathy using shape based features integrated into a convolutional neural network. J Inf Optim Sci 41(1):217–227

    Google Scholar 

  • Stitt AW, Curtis TM, Chen M, Medina RJ, McKay GJ, Jenkins A, Gardiner TA, Lyons TJ, Hammes HP, Simó R, Lois N (2016) The progress in understanding and treatment of diabetic retinopathy. Prog Retin Eye Res 51:156–186

    Article  Google Scholar 

  • Stolte S, Fang R (2020) A survey on medical image analysis in diabetic retinopathy. Med Image Anal 64:101742

    Article  Google Scholar 

  • Sun Y (2019) The neural network of one-dimensional convolution: an example of the diagnosis of diabetic retinopathy. IEEE Access 7:69657–69666

    Article  Google Scholar 

  • Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the 2015 IEEE conference on computer vision and pattern recognition, Boston

  • Ţălu Ş, Călugăru DM, Lupaşcu CA (2015) Characterization of human non-proliferative diabetic retinopathy using the fractal analysis. Int J Ophthalmol 8(4):770–776

    Google Scholar 

  • Tan JH, Fujita H, Sivaprasad S, Bhandary SV, Rao AK, Chua KC, Acharya UR (2017a) Automated segmentation of exudates, haemorrhages, microaneurysms using single convolutional neural network. Inf Sci 420:66–76

    Article  Google Scholar 

  • Tan JH, Acharya UR, Bhandary SV, Chua KC, Sivaprasad S (2017b) Segmentation of optic disc, fovea and retinal vasculature using a single convolutional neural network. J Comput Sci 20:70–79

    Article  Google Scholar 

  • The ACCORD Study Group and ACCORD Eye Study Group (2010) Effects of medical therapies on retinopathy progression in type 2 diabetes. N Engl J Med 363(3):233–244

    Article  Google Scholar 

  • Ting DSW, Cheung CY, Lim G, Tan GSW, Quang ND, Gan A, Hamzah H, Garcia-Franco R, Yeo IYS, Lee SY, Wong EYM, Sabanayagam C, Baskaran M, Ibrahim F, Tan NC, Finkelstein EA, Lamoureux EL, Wong IY, Bressler NM, Sivaprasad S, Varma R, Jonas JB, He MG, Cheng CY, Cheung GCM, Aung T, Hsu W, Lee ML, Wong TY (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 318(22):2211–2223

    Article  Google Scholar 

  • Ting DSW, Pasquale LR, Peng L, Campbell JP, Lee AY, Raman R, Tan GSW, Schmetterer L, Keane PA, Wong TY (2018) Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol 103(2):167–175

    Article  Google Scholar 

  • Torre J, Valls A, Puig D (2020) A deep learning interpretable classifier for diabetic retinopathy disease grading. Neurocomputing 396:465–476

    Article  Google Scholar 

  • Usman I, Almejalli KA (2020) Intelligent automated detection of microaneurysms in fundus images using feature-set tuning. IEEE Access 8:65187–65196

    Article  Google Scholar 

  • Vujosevic S, Aldington SJ, Silva P, Hernández C, Scanlon P, Peto T, Simó PR (2020) Screening for diabetic retinopathy: new perspectives and challenges. Lancet Diabetes Endocrinol 8(4):337–347

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Wang Z, Camino A, Zhang M, Wang J, Hwang TS, Wilson DJ, Huang D, Li D, Jia Y (2017) Automated detection of photoreceptor disruption in mild diabetic retinopathy on volumetric optical coherence tomography. Biomed Opt Express 8(12):5384–5398

    Article  Google Scholar 

  • Wang XN, Dai L, Li ST, Kong HY, Sheng B, Wu Q (2020) Automatic grading system for diabetic retinopathy diagnosis using deep learning artificial intelligence software. Curr Eye Res 45(12):1550–1555

    Article  Google Scholar 

  • Wardoyo S, Pramudyo AS, Rizanti ED, Muttakin I (2016) Exudate and blood vessel feature extraction in diabetic retinopathy patients using morphology operation. Telkomnika 14(4):1493–1501

    Article  Google Scholar 

  • Wei Y, Gong J, Xu Z, Thimmulappa RK, Mitchell KL, Welsbie DS, Biswal S, Duh EJ (2015) Nrf2 in ischemic neurons promotes retinal vascular regeneration through regulation of semaphorin 6A. Proc Natl Acad Sci USA 112(50):e6927–e6936

    Article  Google Scholar 

  • Wong TY, Bressler NM (2016) Artificial intelligence with deep learning technology looks into diabetic retinopathy screening. JAMA Ophthalmol 316(22):2366–2367

    Google Scholar 

  • Wu Z, Shi G, Chen Y, Shi F, Chen X, Li S, Coatrieux G, Yang J, Luo L (2020) Coarse-to-fine classification for diabetic retinopathy grading using convolutional neural network. Artif Intell Med 108:101936

    Article  Google Scholar 

  • Xie Y, Nguyen QD, Hamzah H, Lim G, Bellemo V, Gunasekeran DV, Yip MYT, Lee XQ, Hsu W, Lee ML, Tan CS, Wong HT, Lamoureux EL, Tan GSW, Wong TY, Finkelstein EA, Ting DSW (2020) Artificial intelligence for teleophthalmology-based diabetic retinopathy screening in a national programme: an economic analysis modelling study. Lancet Dig Health 2(5):e240–e249

    Article  Google Scholar 

  • Xu K, Feng D, Mi H (2017) Deep convolutional neural network-based early automated detection of diabetic retinopathy using fundus image. Molecules 22(12):2054

    Article  Google Scholar 

  • Yip MYT, Lim G, Lim ZW, Nguyen QD, Chong CCY, Yu M, Bellemo V, Xie Y, Lee XQ, Hamzah H, Ho J, Tan TE, Sabanayagam C, Grzybowski A, Tan GSW, Hsu W, Lee ML, Wong TY, Ting DSW (2020) Technical and imaging factors influencing performance of deep learning systems for diabetic retinopathy. NPJ Dig Med 3(40):1–12

    Google Scholar 

  • Zago GT, Andreao RV, Dorizzi B, Salles EOT (2020) Diabetic retinopathy detection using red lesion localization and convolutional neural networks. Comput Biol Med 116:103537

    Article  Google Scholar 

  • Zahran MA, Damrawi GE, Amin E, Abdelsalam MM (2020) Enforcing artificial neural network in the early detection of diabetic retinopathy OCTA images analysed by multifractal geometry. J Taibah Univ Sci 14(1):1067–1076

    Article  Google Scholar 

  • Zeng XL, Chen HQ, Lou Y, Ye WB (2019) Automated diabetic retinopathy detection based on binocular Siamese-like convolutional neural network. IEEE Access 7:30744–30753

    Article  Google Scholar 

  • Zhang J, Chen Y, Bekkers E, Wang M, Dashtbozorg B, Romeny BMH (2017) Retinal vessel delineation using a brain-inspired wavelet transform and random forest. Pattern Recogn 69:107–123

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

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Acknowledgements

The authors would like to thank the Editor-in-Chief and the anonymous reviewers for their valuable comments and suggestions.

Funding

The authors (Ganeshsree Selvachandran and Shio Gai Quek) would like to thank the Ministry of Higher Education, Malaysia for funding this research under Grant No. FRGS/1/2020/STG06/UCSI/02/1.

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Authors and Affiliations

  1. Department of Actuarial Science and Applied Statistics, Faculty of Business & Management, UCSI University, Jalan Menara Gading, Cheras, 56000, Kuala Lumpur, Malaysia

    Ganeshsree Selvachandran & Shio Gai Quek

  2. Institute of Computer Science and Digital Innovation, UCSI University, Jalan Menara Gading, Cheras, 56000, Kuala Lumpur, Malaysia

    Raveendran Paramesran

  3. School of Information Science and Technology, Nantong University, Nantong, 226019, People’s Republic of China

    Weiping Ding

  4. VNU Information Technology Institute, Vietnam National University, Hanoi, Vietnam

    Le Hoang Son

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  1. Ganeshsree Selvachandran
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Selvachandran, G., Quek, S.G., Paramesran, R. et al. Developments in the detection of diabetic retinopathy: a state-of-the-art review of computer-aided diagnosis and machine learning methods. Artif Intell Rev 56, 915–964 (2023). https://doi.org/10.1007/s10462-022-10185-6

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