# A Survey on Diabetic Retinopathy Lesion Detection and Segmentation
## Abbreviations  
* [1] IDF Diabetes Atlas. 9 th Edition. Available online: [[https://diabetesatlas.org/atlas/ninth-edition/](https://diabetesatlas.org/atlas/ninth-edition/)]([https://diabetesatlas.org/atlas/ninth-edition/](https://diabetesatlas.org/atlas/ninth-edition/)) (accessed on 30 March 2023). * [2] Understanding Diabetic Retinopathy and How to Reverse It. Available online: [[https://neoretina.com/blog/diabetic-retinopathy-can-it-be-reversed/](https://neoretina.com/blog/diabetic-retinopathy-can-it-be-reversed/)]([https://neoretina.com/blog/diabetic-retinopathy-can-it-be-reversed/](https://neoretina.com/blog/diabetic-retinopathy-can-it-be-reversed/)) (accessed on 30 March 2023). * [3] Automated Retinal Image Analysis (ARIA) Data Set--[PERSON]. Available online: [[https://www.damianjifarnell.com/?page_id=Z76](https://www.damianjifarnell.com/?page_id=Z76)]([https://www.damianjifarnell.com/?page_id=Z76](https://www.damianjifarnell.com/?page_id=Z76)) (accessed on 30 March 2023). * [4] [PERSON]; [PERSON]; [PERSON] Retinal vessel segmentation using neural network. _IET Image Process._**2018**, 12, 669-678. [CrossRef]
* [5] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Detection and counting the microaneurysms using image processing techniques. _Int. J. Appl. Inform. Syst_**2013**, 6, 11-17. * [6] [PERSON]; [PERSON] Screening diabetic retinopathy in developing countries using retinal images. _Appl. Med. Inform._**2015**, 36, 13-22. * [7] [PERSON]; [PERSON] Automatic exudate detection by fusing multiple active contours and regionwise classification. _Comput. Biol. Med._**2014**, 54, 156-171. [CrossRef] [PubMed]
* [8] [PERSON]; [PERSON] Automated feature extraction in color retinal images by a model based approach. _IEEE Trans. Biomol. Eng._**2004**, 51, 246-254. [CrossRef] [PubMed]
* [9] [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] Detection of hard exudates in retinal images using a radial basis function classifier. _Ann. Biomol. Eng._**2009**, 37, 1448-1463. * [10] [PERSON]; [PERSON]; [PERSON]; [PERSON], [PERSON]; [PERSON] [PERSON] Automatic image processing algorithm to detect hard exudates based on mixture models. In Proceedings of the 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, New York, NY, USA, 30 August-3 September 2006; pp. 4453-4456. * [11] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Automatic detection of red lesions in digital color fundus photographs. _IEEE Trans. Med. Imaging_**2005**, 24, 584-592. [CrossRef] [PubMed]
* [12] [PERSON]; [PERSON] A novel retinal image segmentation using rSVM boosted convolutional neural network for exudates detection. _Biomed. Signal Process. Control_**2021**, 68, 102785. * [13] [PERSON]; [PERSON] Blood vessel segmentation using hybrid median filtering and morphological transformation. In Proceedings of the 2017 13 th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), Jaipur, India, 4-7 December 2017; pp. 151-157. * [14] [PERSON]; [PERSON] Retinal blood vessel segmentation using curvelet transform and morphological reconstruction. In Proceedings of the 2013 IEEE International Conference ON Emerging Trends in Computing, Communication and Nanotechnology (ICECCN), Tirunleveli, India, 25-26 March 2013; pp. 570-575. * [15] [PERSON]; [PERSON]; [PERSON] Automated optical disc segmentation and blood vessel extraction for fundus images using ophthalmic image processing. In Proceedings of the International Conference on Advanced Informatics for Computing Research, Gurugram, India, 18-19 December 2018; pp. 182-194. * [PERSON] and [PERSON] (2019) [PERSON]; [PERSON] An automatic detection of blood vessel in retinal images using convolution neural network for diabetic retinopathy detection. _Patt. Recogn. Image Anal._**2019**, _29_, 533-545. * [PERSON] (2018) [PERSON] Exudate detection for diabetic retinopathy with circular Hough transformation and convolutional neural networks. _Expert Syst. Appl._**2018**, _114_, 289-295. [CrossRef]
* [PERSON] et al.