Open Access
Subscription Access
Diabetes Retinopathy Detection : A Survey
Subscribe/Renew Journal
Diabetic Retinopathy (DR) is a disease that may cause vision impairment. The early detection of this disease is important. This work surveys the different detection and feature selection techniques involved in the detection of this disease. This can be done by studying the lesions found in the human retina using fundus images. This work also reviews the various feature extraction techniques such as Support Vector Machines, Neural Networks, and Convolutional Networks. Deep learning algorithms are discussed and the different ways of implementation of the automated system are discussed.
Keywords
Convolutional Neural Networks, Deep Learning, Diabetes Retinopathy, Feature Selection, Lesions, Fundus Images, Neural Networks, Support Vector Machines, Vision Impairment.
Manuscript Received: January 8, 2020; Revised: January 16, 2020; Accepted: January 18, 2020. Date of Publication: February 5, 2020.
User
Subscription
Login to verify subscription
Font Size
Information
- Y. S. Kanungo, B. Srinasan, and S. Choudhary, "Detecting diabetic retinopathy using deep learning," in 2017 2nd IEEE Int. Conf. on Recent Trends in Electron., Inform. & Communication Technol., pp. 801-804, 2017. https://dx.doi.org/10.1109/rteict.2017.8256708
- "Feature extraction from the fundus images for the diagnosis of diabetic retinopathy," in 2015 Int. Conf. on Emerging Res. in Electron., Comput. Sci. and Technol., pp. 240-245, 2015. https://dx.doi.org/10.1109/ERECT.2015.7499020
- D. S. Sisodia, S. Nair, and P. Khobragade, "Diabetic retinal fundus images: Preprocessing and feature extraction for early detection of diabetic retinopathy," Biomedical and Pharmacology J., vol. 10, no. 2, pp. 615-626, 2017. https://dx.doi.org/10.13005/bpj/1148
- J. Lachure, A. V. Deorankar, S. Lachure, S. Gupta, and R. Jadhav, "Diabetic retinopathy using morphological operations and machine learning," in 2015 IEEE Int. Advance Computing Conf., pp. 617-622, 2015. https://dx.doi.org/10.1109/IADCC.2015.7154781
- K. Latha and S. G. Durga, "Diagnosis of diabetic retinopathy based on feature extraction," Int. J. of Innovative Res. in Sci., Eng. and Technol., vol. 3, no. 3, 2014.
- S. Roychowdhury, D. D. Koozekanani, and K. K. Parhi, "DREAM: diabetic retinopathy analysis using machine learning," IEEE J. of Biomedical and Health Informatics, vol. 18, no. 5, pp. 1717-1728, 2014.
- C. JayaKumari and R. Maruthi, "Detection of hard exudates in color fundus images of the human retina," Procedia Eng., vol. 30, pp. 297-302, 2012. Doi: https://dx.doi.org/10.1016/j.proeng.2012.01.864
- R. Gargeya and T. Leng, "Automated identification of diabetic retinopathy using deep learning," Ophthalmology, vol. 124, no. 7, pp. 962-969, 2017.
- V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, and S. Venugopalan, "Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs," Jama, vol. 316, no. 22, pp. 2402-2410, 2016. https://dx.doi.org/10.1001/jama.2016.17216
- R. Sivakumar, G. Ravindran, M. Muthayya, S. Lakshminarayanan, and C. U. Velmurughendran, "Diabetic retinopathy classification," in TENCON 2003. Conf. on Convergent Technologies for Asia-Pacific Region, vol. 1, pp. 205-208, 2003.
- S. Seth, and B. Agarwal, "A hybrid deep learning model for detecting diabetic retinopathy," J. of Statist. and Manage. Syst., vol. 21, no. 4, pp. 569-574, 2018.
- T. Chandrakumar and R. Kathirvel, "Classifying diabetic retinopathy using deep learning architecture," Int. J. Eng. ReTechnol., vol. 5, no. 6, pp. 19-24, 2016. http://dx.doi.org/10.17577/IJERTV5IS060055
Abstract Views: 258
PDF Views: 1