Deep Convolutional Neural Network for Image Quality Assessment and Diabetic Retinopathy Grading
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-33658-4_4
Reference16 articles.
1. Burlina, P.M., Joshi, N., Pacheco, K.D., Liu, T.A., Bressler, N.M.: Assessment of deep generative models for high-resolution synthetic retinal image generation of age-related macular degeneration. JAMA Ophthalmol. 137(3), 258–264 (2019)
2. Casanova, R., Saldana, S., Chew, E.Y., Danis, R.P., Greven, C.M., Ambrosius, W.T.: Application of random forests methods to diabetic retinopathy classification analyses. PLoS ONE 9(6), e98587 (2014)
3. Dai, L., et al.: A deep learning system for detecting diabetic retinopathy across the disease spectrum. Nat. Commun. 12(1), 1–11 (2021)
4. Gargeya, R., Leng, T.: Automated identification of diabetic retinopathy using deep learning. Ophthalmology 124(7), 962–969 (2017)
5. Gulshan, V., et al.: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316(22), 2402–2410 (2016)
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