Deep Learning Model for Diagnosing the Severity of Diabetic Retinopathy
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-9037-5_32
Reference18 articles.
1. Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J et al (2016) Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316(22):2402–2410
2. Rajkumar RS, Jagathishkumar T, Ragul D, Selvarani AG (2021) Transfer learning approach for diabetic retinopathy detection using residual network. In: 2021 6th International conference on inventive computation technologies (ICICT), pp 1189–1193. https://doi.org/10.1109/ICICT50816.2021.9358468
3. Vora P, Shrestha S (2020) Detecting diabetic retinopathy using embedded computer vision. Appl Sci 10:7274. https://doi.org/10.3390/app10207274
4. Bajwa MN et al (2019) Combining fine- and coarse-grained classifiers for diabetic retinopathy detection. ArXiv abs/2005.14308
5. Chakrabarty N (2019) A deep learning method for the detection of diabetic retinopathy. https://doi.org/10.1109/UPCON.2018.8596839
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