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: 23

1- 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]

2- 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]

3- 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]

4- 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]

5- 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]

6- 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]

7- Selvachandran G, Quek S, Paramesran R, Ding W, Son L. Developments in the detection of diabetic retinopathy: a state-of-the-art review of computer-aided diagnosis and machine learning methods. Artif Intell Rev. 2023;56(2):915 [Crossref]

8- 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]

9- Kalyani G, Janakiramaiah B, Karuna A, Prasad L. Diabetic retinopathy detection and classification using capsule networks. Complex Intell Syst. 2023;9(3):2651 [Crossref]

10- Shanthi T, Sabeenian R. Modified Alexnet architecture for classification of diabetic retinopathy images. Computers & Electrical Engineering. 2019;76:56 [Crossref]

11- 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]

12- 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]

13- 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]

14- Bhardwaj C, Jain S, Sood M. Automated Diagnostic Hybrid Lesion Detection System for Diabetic Retinopathy Abnormalities. SWCC. 2020;10(4):494 [Crossref]

15- Thanikachalam V, G. Kavitha M, Sivamurugan V. Diabetic Retinopathy Diagnosis Using Interval Neutrosophic Segmentation with Deep Learning Model. 2023;44(3):2129 [Crossref]

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