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All issues Volume 32 (2020) ITM Web Conf., 32 (2020) 01012 References
Open Access
Issue
ITM Web Conf.
Volume 32, 2020
International Conference on Automation, Computing and Communication 2020 (ICACC-2020)
Article Number 01012
Number of page(s) 5
Section Automation
DOI https://doi.org/10.1051/itmconf/20203201012
Published online 29 July 2020
  1. Kertes PJ, Johnson TM, eds. (2007). Evidence Based Eye Care. Philadelphia, PA: Lippincott Williams and Wilkins. ISBN 978-0-7817-6964-8. [Google Scholar]
  2. Tapp RJ, Shaw JE, Harper CA, de Courten MP, Balkau B, McCarty DJ, Taylor HR, Welborn TA, Zim-met PZ (June 2003). “The prevalence of and factors associated with diabetic retinopathy in the Australian population”. Diabetes Care. 26 (6): 1731-1731. doi: 10.2337/diacare.26.6.1731 [Google Scholar]
  3. EverydayHealth.com. (2020). Diabetic Retinopa thy - Types, Symptoms, Causes and Compli cations | Everyday Health, [online] Available at: https://www.everydayhealth.com/diabetic-retinopathy/guide/ [Google Scholar]
  4. MedicineNet. (2020). Definition of Arteriosclerotic retinopathy, [online] Available at: https://www.medicinenet.com/script/main/art.asp?articlekey=24652 [Google Scholar]
  5. Cold, F, Health, E., Disease, H., Disease, L., Management, P., Conditions, S., Problems, S., Disorders, S., Checker, S., Blogs, W, Boards, M., Answers, Q., Guide, I., Doctor, F, A-Z, C, A-Z, S., Medications, M., Identifier, P., Interactions, C, Drugs, C, Pregnant, T., Management, D., Obesity, W, Recipes, F., Exercise, E, Beauty, H., Balance, H, Relationships, S., Care, O., Health, W, Health, M., Well, A., Sleep, H., Teens, H., Pregnant, G., Trimester, F, Trimester, S., Trimester, T, Baby, N., Health, C, Vaccines, C, Kids, R., Cats, H., Dogs, H., Updates, C, Unbeatable, C, Back, I., Play Golf, L., Peak, E, Boards, M., Blogs, W and Center, N. (2020). Diabetic Retinopathy: Risks, Treatments, Prevention, [online] WebMD. Available at: https://www.webmd.com/diabetes/diabetic-retinopathyl [Google Scholar]
  6. Gulshan, V., Peng, L., Coram, M., Stumpe, M., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., Kim, R., Raman, R., Nelson, P., Mega, J. and Webster, D. (2016). Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA, 316(22), p. 2402. [Google Scholar]
  7. Pratt, H., Coenen, E, Broadbent, D., Harding, S. and Zheng, Y. (2016). Convolutional Neural Networks for Diabetic Retinopathy. Procedia Computer Science, 90, pp.200-205. [Google Scholar]
  8. Triwijoyo, B., Budiharto, W and Abdurachman, E. (2017). The Classification of Hypertensive Retinopathy using Convolutional Neural Network. Procedia Computer Science, 116, pp.166-173. [Google Scholar]
  9. Wan, S., Liang, Y. and Zhang, Y. (2018). Deep convolutional neural networks for diabetic retinopathy detection by image classification. Computers and Electrical Engineering, 72, pp.274-282. [Google Scholar]
  10. Pires, R., Avila, S., Wainer, J., Valle, E., Abramoff, M. and Rocha, A. (2019). A data-driven approach to referable diabetic retinopathy detection. Artificial Intelligence in Medicine, 96, pp.93-106. [Google Scholar]
  11. Shanthi, T. and Sabeenian, R. (2019). Modified Alexnet architecture for classification of diabetic retinopathy images. Computers and Electrical Engineering, 76, pp.56-64. [Google Scholar]
  12. Porod, Wolfgang and Werblin, Frank and Chua, Leon and Roska, Tamas and Rodriguez-Vazquez, Angel and Roska, Botond and Fay, Patrick and Bernstein, Gary and Huang, Yih-Fang and Csurgay, Arpdd. (2004). Bio-Inspired Nano-Sensor-Enhanced CNN Visual Computer. Annals of the New York Academy of Sciences. 1013. 92-109. 10.1196/annals.l305.011. [Google Scholar]
  13. S. Srivastava, S. Prabhu, S. Ramesh, S. Pratapneni, A. Abraham and S.V. Bhandary, “Visualizing the Indicators of Diabetic Retinopathy Learnt by Convolutional Neural Networks,” 2017 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Coimbatore, 2017, pp. 1-3. [Google Scholar]
  14. Wilkes, B. (2009). Central Mersey Diabetic Retinopathy Screening Programme, [online] pp.45-50. [Google Scholar]
  15. DocShop. (2020). Diabetic Retinopathy - Causes, Diagnosis, Symptoms, Risks, [online] Available at: https://www.docshop.com/education/vision/eye-diseases/diabetic-retinopathy. [Google Scholar]
  16. DeMuro, J. (2020). What is a neural network?. [online] TechRadar. Available at: https://www.techradar.com/news/what-is-a-neural-network. [Google Scholar]
  17. X. Wang, Y Lu, Y Wang and W. Chen, “Diabetic Retinopathy Stage Classification Using Convolutional Neural Networks,” 2018 IEEE International Conference on Information Reuse and Integration (IRI), Salt Lake City, UT, 2018, pp. 465-471. [Google Scholar]
  18. Kwasigroch, Arkadiusz, Bartlomiej larzembinski, and Michal Grochowski. “Deep CNN based decision support system for detection and assessing the stage of diabetic retinopathy.” 2018 International Interdisciplinary PhD Workshop (IIPhDW). IEEE, 2018 [Google Scholar]
  19. Sharma, H., Singh, A., Chandel, A., Singh, P. and Sapkal, P. (2019). Detection of Diabetic Retinopathy Using Convolutional Neural Network. SSRN Electronic lournal. [Google Scholar]
  20. Torrey, Lisa and Jude Shavlik. “Transfer Learning.” Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques. IGI Global, 2010. 242-264. [Google Scholar]

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