Diabetic Retinopathy Detection Using Deep Learning Models
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-19-2541-2_7
Reference26 articles.
1. A vitamin A analogy may help treat diabetic retinopathy (elsevier.com)
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4. P. M. Ebin, P. Ranjana, An approach using transfer learning to disclose diabetic retinopathy in early stage, in 2020 International Conference on Futuristic Technologies in Control Systems and Renewable Energy (ICFCR) (2020), pp 1–4. https://doi.org/10.1109/ICFCR50903.2020.9249988
5. F. Alzami, Abdussalam, R.A. Megantara, A.Z. Fanani and Purwanto, Diabetic retinopathy grade classification based on fractal analysis and random forest, in 2019 International Seminar on Application for Technology of Information and Communication (iSemantic) (2019), pp. 272–276. https://doi.org/10.1109/ISEMANTIC.2019.8884217
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1. DeepRetina: A Deep Learning Approach for Diabetic Retinopathy Detection and Stage Classification;2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS);2024-04-18
2. Dynamic Convolutional Attention for Classification of Diabetic Retinopathy;2023 IEEE 7th Conference on Information and Communication Technology (CICT);2023-12-15
3. An Automated Multi-Level Convolutional Neural Network Approach for Classification of White Blood Cells;2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT);2023-07-06
4. A Prospective Study on Diabetic Retinopathy Detection Based on Modify Convolutional Neural Network Using Fundus Images at Sindh Institute of Ophthalmology & Visual Sciences;Diagnostics;2023-01-20
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