Detection of Diabetic Retinopathy Using CNN
Author:
Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-030-99197-5_8
Reference20 articles.
1. Solomon, S.D., et al.: Erratum. diabetic retinopathy: a position statement by the American diabetes association. Diabetes Care 40(6), 412–148 (2017)
2. Carrera, E.V., Gonzalez, A., Carrera, R.: Automated detection of diabetic retinopathy using SVM. In: IEEE XXIV International Conference on Electronics, Electrical Engineering and Computing (2017)
3. Khojasteh, P., Aliahmad, B., Kumar, D.K.: Fundus images analysis using deep features for detection of exudates, hemorrhages and microaneurysms. BMC Ophthalmol. 18(1) (2018). https://doi.org/10.1186/s12886-018-0954-4
4. Rajalakshmi, R., Subashini, R., Anjana, R.M., Mohan, V.: Automated diabetic retinopathy detection in smartphone-based fundus photography using artificial intelligence. Eye 32(6), 1138–1144 (2018)
5. Ray, S., Adhikary, P., Chatterjee, S., Midya, P., Palui, R., Ghosh, S.: A review on measurement of different parameters from retinal images to detect Glaucoma and Cataract. In: 2019 3rd International Conference on Electronics, Materials Engineering & Nano-Technology, IEMENTech (2019)
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