Detection and Grading of Diabetic Retinopathy Using Deep Learning
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-3191-6_43
Reference17 articles.
1. Abbas, Q., Ibrahim, M.E.A.: DenseHyper: an automatic recognition system for detection of hypertensive retinopathy using dense features transform and deep-residual learning. Multimed Tools Appl. 79, 31595–31623 (2020). https://doi.org/10.1007/s11042-020-09630-x
2. AlBadawi, S., Fraz, M.M.: Arterioles and venules classification in retinal ımages using fully convolutional deep neural network. In: Campilho, A., Karray, F., ter Haar Romeny, B. (Eds.) Image Analysis and Recognition. ICIAR 2018. Lecture Notes in Computer Science, vol. 10882. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93000-8_75
3. Asiri, N., Hussain, M., Al Adel, F., Alzaidi, N.: Deep learning based computer-aided diagnosis systems for diabetic retinopathy: a survey. Artif. İntell. Med. 99, 101701 (2019)
4. Gao, Y., Yu, X., Wu, C., Zhou, W., Lei, X., Zhuang, Y.: Automatic optic disc segmentation based on modified local image fitting model with shape prior information. J. Healthc. Eng. 2019 (2019)
5. Yin, X.-X., Irshad, S., Zhang, Y.: Artery/vein classification of retinal vessels using classifiers fusion. Health Inf. Sci. Syst. 7, 1–14 (2019)
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