Lee R, Wong TY, Sabanayagam C (2015) Epidemiology of diabetic retinopathy, diabetic macular edema, and related vision loss. Eye and vision 2(1):17

Article  PubMed  PubMed Central  Google Scholar 

Wilkinson C, Ferris FL, Klein RE et al (2003) Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales. Ophthalmology 110(9):1677–1682

Article  CAS  PubMed  Google Scholar 

Yau JW, Rogers SL, Kawasaki R et al (2012) Global prevalence and major risk factors of diabetic retinopathy. Diabetes Care 35(3):556–564

Article  PubMed  PubMed Central  Google Scholar 

Ahmed NGA, Hamza MF, Hassan SN (2023) Knowledge, practice and attitude of diabetic patients regarding prevention of diabetic retinopathy. J Surv Fisher Sci 10(3S):3896–3908

Google Scholar 

Lyssekboron A, Wylegala A, Polanowska K, Krysik K, Dobrowolski D (2017) Longitudinal changes in retinal nerve fiber layer thickness evaluated using avanti rtvue-xr optical coherence tomography after 23g vitrectomy for epiretinal membrane in patients with open-angle glaucoma. J Healthc Eng 2017:4673714–4673714

PubMed  Google Scholar 

Salz DA, Witkin AJ (2015) Imaging in diabetic retinopathy. Middle East Afr J Ophthalmol 22(2):145–150

Article  PubMed  PubMed Central  Google Scholar 

Bernardes R, Serranho P, Lobo C (2011) Digital ocular fundus imaging: a review. Ophthalmological 226(4):161–181

Article  Google Scholar 

Chew EY, Ferris FL (2006) Chapter 67—nonproliferative diabetic retinopathy. In: Ryan SJ, Hinton DR, Schachat AP, Wilkinson CP (eds.) Retina, Fourth edition edn., Mosby, Edinburgh, pp 1271–1284

Eftekhari N, Pourreza HR, Masoudi M, Ghiasishirazi K, Saeedi E (2019) Microaneurysm detection in fundus images using a two-step convolutional neural network. Biomed Eng Online 18(1):67

Article  PubMed  PubMed Central  Google Scholar 

Chaturvedi SS, Gupta K, Ninawe V, Prasad PS (2019) Advances in computer-aided Diagnosis of diabetic retinopathy. arXiv e-prints, 1909–09853

Saha S, Xiao D, Bhuiyan A, Wong TY, Kanagasingam Y (2019) Color fundus image registration techniques and applications for automated analysis of diabetic retinopathy progression: a review. Biomed Signal Process Control 47:288–302

Article  Google Scholar 

Salamat N, Missen MMS, Rashid A (2019) Diabetic retinopathy techniques in retinal images: a review. Artif Intell Med 97:168–188

Article  PubMed  Google Scholar 

Biyani RS, Patre BM (2018) Algorithms for red lesion detection in diabetic retinopathy: a review. Biomed Pharmacother 107:681–688

Article  CAS  PubMed  Google Scholar 

Chen Z, Chen B et al (2022) A preliminary observation on rod cell photo biomodulation in treating diabetic macular edema. Adv Ophthalmol Pract Res 2(2):10051

Google Scholar 

Lazar I, Hajdu A (2011) Microaneurysm detection in retinal images using a rotating cross-section-based model. In: 2011 IEEE international symposium on biomedical imaging: from nano to macro. Piscataway: IEEE, p 1405–1409

Lazar I, Hajdu A (2013) Retinal microaneurysm detection through local rotating cross-section profile analysis. IEEE TransMed Imaging 32(2):400–407

Article  Google Scholar 

Niemeijer M, Van Ginneken B, Staal J, Suttorp-Schulten MS, Abràmoff MD (2005) Automatic detection of red lesions in digital color fundus photographs. IEEE Trans Med Imaging 24(5):584–592

Article  PubMed  Google Scholar 

Giancardo L, Meriaudeau F, Karnowski TP, Li Y, Tobin KW, Chaum E (2011) Microaneurysm detection with radon transform-based classification on retina images. In: international conference of the IEEE engineering in medicine and biology society, pp 5939–5942

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

Article  ADS  Google Scholar 

Ram K, Joshi GD, Sivaswamy J (2011) A successive clutter-rejection-based approach for early detection of diabetic retinopathy. IEEE Trans Biomed Eng 58(3):664–673

Article  PubMed  Google Scholar 

Saha, R., Chowdhury, A. R., & Banerjee, S. (2016). Diabetic retinopathy related lesions detection and classification using machine learning technology. In Artificial Intelligence and Soft Computing: 15th International Conference, ICAISC 2016, Zakopane, Poland, June 12–16, 2016, Proceedings, Part II 15 (pp. 734–745). Springer International Publishing.

Google Scholar 

Zhou W, Wu C, Chen D, Yi Y, Du W (2017) Automatic microaneurysm detection using the sparse principal component analysis-based unsupervised classification method. IEEE Access 5:2563–2572

Article  Google Scholar 

Srivastava R, Duan L, Wong DWK, Liu J, Wong TY (2016) Detecting retinal microaneurysms and haemorrhages with robustness to the presence of blood vessels. Comput Methods Progr Biomed 138:83–91

Article  Google Scholar 

Wu B, Zhu W, Shi F, Zhu S, Chen X (2017) Automatic detection of microaneurysms in retinal fundus images. Comput Med Imaging Gr 55:106–112

Article  Google Scholar 

Wang S, Tang HL, Turk LA, Hu Y, Sanei S, Saleh GM, Peto T (2017) Localizing microaneurysms in fundus images through singular spectrum analysis. IEEE Trans Biomed Eng 64(5):990–1002

Article  ADS  PubMed  Google Scholar 

Derwin DJ, Selvi ST, Singh OJ (2019) Secondary observer system for detection of microaneurysms in fundus images using texture descriptors. J Digit Imaging 33(1):159–167

Article  PubMed Central  Google Scholar 

Mizutani A, Muramatsu C, Hatanaka Y, Suemori S, Hara T, Fujita H(2009) Automated microaneurysm detection method based on double-ring filter in retinal fundus images. In: proceedings of SPIE medical imaging, vol 7260, 2009, pp 72 601N–72 601N–8

Zhang X (2014) Image processing methods for computer-aided screening of diabetic retinopathy, Ph.D. dissertation, EcoleNationaleSup´erieuredesMines de Paris, Paris

Adal KM, Sidib D, Ali S, Chaum E, Karnowski TP, Mriaudeau F (2014) Automated detection of microaneurysms using scale-adapted blob analysis and semi-supervised learning. Comput Methods Programs Biomed 114(1):1–10

Article  PubMed  Google Scholar 

Dai B, Wu X, Bu W (2016) Retinal microaneurysms detection using gradient vector analysis and class imbalance classification. PLoS ONE 11(8):1–23

Article  CAS  Google Scholar 

Budak U, Şengür A, Guo Y, Akbulut Y (2017) A novel microaneurysms detection approach based on convolutional neural networks with reinforcement sample learning algorithm. Health Inf Sci Syst 5(1):14

Article  PubMed  PubMed Central  Google Scholar 

Wang S, Tang HL, Hu Y et al (2017) Localizing microaneurysms in fundus images through singular spectrum analysis. IEEE Trans Biomed Eng 64(5):990–1002

Article  ADS  PubMed  Google Scholar 

Seoud L, Hurtut T, Chelbi J, Cheriet F, Langlois JMP (2016) Redlesion detection using dynamic shape features for diabetic retinopathy screening. IEEE Trans Med Imag 35(4):1116–1126

Article  Google Scholar 

Eftekhari N et al (2019) Microaneurysm detection in fundus images using a two-step convolutional neural network. Biomed Eng Online 18(1):1–16

Article  Google Scholar 

Dashtbozorg B et al (2018) Retinal microaneurysms detection using local convergence index features. IEEE Trans Image Process 27(7):3300–3315

Article  ADS  MathSciNet  PubMed  Google Scholar 

Habib MM et al (2017) Detection of microaneurysms in retinal images using an ensemble classifier. Inf Med Unlocked 9:44–57

Article  Google Scholar 

Lazar I, Hajdu A (2013) Retinal microaneurysm detection through local rotating cross-section profile analysis. IEEE Trans Med Imag 32(2):400–407

Article  Google Scholar 

Zhang X (2014) Image processing methods for computer-aided screening of diabetic retinopathy, Ph.D. dissertation, EcoleNationaleSuperieure des ´ Mines de Paris, Paris

Ali Shah SA, Laude A, Faye I, Tang TB (2016) Automated microaneurysm detection in diabetic retinopathy using curvelet transform. J Biomed Opt 21(10):101404

Article  PubMed  Google Scholar 

Wang S, Tang HL, Turk LIA et al (2017) Localizing microaneurysms in fundus images through singular spectrum analysis. IEEE Trans Biomed Eng 64(5):990–1002

Article  ADS  PubMed  Google Scholar 

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

Article  Google Scholar 

Suchetha M, Sai Ganesh N, Raman R, Edwin Dhas D (2021) Region of interest-based predictive algorithm for subretinal hemorrhage detection using faster R-CNN. Soft Comput 25(24):15255–15268

Article  CAS  PubMed  PubMed Central  Google Scholar 

Yadav D, Karn AK, Giddalur A, Dhiman A, Sharma S, Yadav AK (2021) Microaneurysm detection using color locus detection method. Measurement 176:109084

Article  Google Scholar 

Dai B, Xiangqian Wu, Wei Bu (2016) Retinal microaneurysms detection using gradient vector analysis and class imbalance classification. PLoS ONE 11(8):e0161556

Article  PubMed  PubMed Central  Google Scholar 

Bradley D, Roth G (2007) Adapting thresholding using the integral image. J Gr Tools 12(2):13–21

Article  Google Scholar 

Naoum RS, Abid NA, Al-Sultani ZN (2012) An enhanced resilient backpropagation artificial neural network for the intrusion detection system. Int J Comput Sci Netw Secur (IJCSNS) 12(3):11

Google Scholar 

MESSIDOR: Methods for evaluating segmentation and indexing techniques dedicated to retinal ophthalmology. https://www.scribd.com/document/70380364/Messidor-Abstract-En

Kauppi T, Kalesnykiene V, Kamarainen J, Lensu L, Sorri I, Raninen A, Voutilainen R, Uusitalo H, K¨alvi¨ainen H, Pietil¨a J (2007) DIARETDB1diabeticretinopathy database and evaluation protocol. In: 11th conference on medical image understanding and analysis

Decenciere E, Cazuguel G, Zhang X et al (2013) TeleOphta: Machine learning and image processing methods for teleophthalmology. IRBM 34(2):196–203

Article  Google Scholar 

Niemeijer M, Van Ginneken B, Cree MJ et al (2010) Retinopathy online challenge: automatic detection of microaneurysms in digital color fundus photographs. IEEE Trans Med Imag 29(1):185–195

Article  Google Scholar