Open Access
| Issue |
ITM Web Conf.
Volume 44, 2022
International Conference on Automation, Computing and Communication 2022 (ICACC-2022)
|
|
|---|---|---|
| Article Number | 03027 | |
| Number of page(s) | 6 | |
| Section | Computing | |
| DOI | https://doi.org/10.1051/itmconf/20224403027 | |
| Published online | 05 May 2022 | |
- P.M. Ebin, P. Ranjana, “An Approach Using Transfer Learning to Disclose Diabetic Retinopathy in Early Stage”, Presented at 2020 International Conference on Futuristic Technologies in Control Systems & Renewable Energy (ICFCR), Malappuram, India, (Sept 2020). [Google Scholar]
- S. Mishra, S. Hanchate, Z. Saquib, “Diabetic Retinopathy Detection using Deep Learning”, Presented at 2020 International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE), Bengaluru, India, (Oct 2020). [Google Scholar]
- A. Habib Raj, A. Mamun, F. Faruk, “CNN Based Diabetic Retinopathy Status Prediction Using Fundus Images”, Presented at IEEE Region 10 Symposium (TENSYMP), Dhaka, Bangladesh, (June 2020). [Google Scholar]
- N. Doshi, U. Oza and P. Kumar, “Diabetic Retinopathy Classification using Downscaling Algorithms and Deep Learning”, Presented at 7th International Conference on Signal Processing and Integrated Networks (SPIN), Noida, India, (Feb. 2020). [Google Scholar]
- J. Yadav, M. Sharma and V. Saxena, “Diabetic Retinopathy Detection using feedforward Neural Network”, Presented at Tenth International Conference on Contemporary Computing (IC3), 9 August 2017). [Google Scholar]
- Y. Kanungo, B. Srinivasan and S. Choudhary, “Detecting Diabetic Retinopathy using Deep Learning”, Presented at IEEE International Conference on Recent Trends in Electronics Information & Communication Technology (RTEICT), (May 2017), India. [Google Scholar]
- S. Karki and P. Kulkarni, “Diabetic Retinopathy Classification using a Combination of EfficientNets”, Presented at International Conference on Emerging Smart Computing and Informatics (ESCI), Pune, India, (March 2021). [Google Scholar]
- Z. Gao, J. Li, J. Guo, Y. Chen, Z. Yi and J. Zhong, “Diagnosis of Diabetic Retinopathy Using Deep Neural Networks”. [Google Scholar]
- S. Thorat, A. Chavan, P. Sawant, S. Kulkarni, N. Sisodiya and A. Kolapkar, “Diabetic Retinopathy Detection by means of Deep Learning”, Presented at 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS) Madurai, India, (May 2021). [Google Scholar]
- S. M. Saiful and M. M. Hasan, “Deep Learning based Early Detection and Grading of Diabetic Retinopathy Using Retinal Fundus Images,” arXiv preprint arXiv:1812.10595. (Dec 2018). [Google Scholar]
- F. DS, A. L, G. TW, K. GL, B. G, C. JD, K. R, and A. D. Association, “Diabetic retinopathy,” Diabetes care, vol. 26, no. 1, pp. 226–229, (2003). [CrossRef] [Google Scholar]
- D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot et al., “Mastering the game of go with deep neural networks and tree search,” Nature, vol. 529, no. 7587, p. 484, (2016). [CrossRef] [Google Scholar]
- A. D. Hoover, V. Kouznetsova, and M. Goldbaum, “Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response,” IEEE Transactions on Medical Imaging, vol. 19, no. 3, pp. 203–210, (March 2000). [CrossRef] [Google Scholar]
- T. Walter, P. Massin, A. Erginay, R. Ordonez, C. Jeulin, and J.-C. Klein, “Automatic detection of microaneurysms in color fundus images,” Medical Image Analysis, vol. 11, no. 6, pp. 555–566, (2007). [CrossRef] [Google Scholar]
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