J Anal Res Clin Med. 2016;4(2): 104-109. doi: 10.15171/jarcm.2016.017

Original Article

Automatic detection of retinal exudates in fundus images of diabetic retinopathy patients

Mahsa Partovi 1, Seyed Hossein Rasta 2 * , Alireza Javadzadeh 3

Cited by CrossRef: 15

1- Shankar K, Perumal E, Vidhyavathi R. Deep neural network with moth search optimization algorithm based detection and classification of diabetic retinopathy images. SN Appl Sci. 2020;2(4) [Crossref]
2- Kalyani G, Janakiramaiah B, Karuna A, Prasad L. Diabetic retinopathy detection and classification using capsule networks. Complex Intell Syst. 2021; [Crossref]
3- Shanthi T, Sabeenian R. Modified Alexnet architecture for classification of diabetic retinopathy images. Computers & Electrical Engineering. 2019;76:56 [Crossref]
4- Shankar K, Sait A, Gupta D, Lakshmanaprabu S, Khanna A, Pandey H. Automated detection and classification of fundus diabetic retinopathy images using synergic deep learning model. Pattern Recognition Letters. 2020;133:210 [Crossref]
5- Al-Jarrah M, Shatnawi H. Non-proliferative diabetic retinopathy symptoms detection and classification using neural network. Journal of Medical Engineering & Technology. 2017;41(6):498 [Crossref]
6- Saeed E, Szymkowski M, Saeed K, Mariak Z. An Approach to Automatic Hard Exudate Detection in Retina Color Images by a Telemedicine System Based on the d-Eye Sensor and Image Processing Algorithms. Sensors. 2019;19(3):695 [Crossref]
7- Vaishnavi J, Ravi S, Anbarasi A. An efficient adaptive histogram based segmentation and extraction model for the classification of severities on diabetic retinopathy. Multimed Tools Appl. 2020;79(41-42):30439 [Crossref]
8- Amin J, Sharif M, Yasmin M, Ali H, Fernandes S. A method for the detection and classification of diabetic retinopathy using structural predictors of bright lesions. Journal of Computational Science. 2017;19:153 [Crossref]
9- Al-Sharfaa A, Yousif A, Al-Saadi E. Localization of Optic Disk and Exudates Detection in Retinal Fundus Images. J Phys: Conf Ser. 2021;1804(1):012128 [Crossref]
10- Mohammadi F, Esmaeili M, Javadzadeh A, Tabar H, Rasta S. The computer based method to diabetic retinopathy assessment in retinal images: a review. Electron J Gen Med. 2019;16(2):em114 [Crossref]
11- Hamad H, Dwickat T, Tegolo D, Valenti C. Exudates as Landmarks Identified through FCM Clustering in Retinal Images. Applied Sciences. 2020;11(1):142 [Crossref]
12- Shankar K, Zhang Y, Liu Y, Wu L, Chen C. Hyperparameter Tuning Deep Learning for Diabetic Retinopathy Fundus Image Classification. IEEE Access. 2020;8:118164 [Crossref]