EfficientNet Empowered by Dendritic Learning for Diabetic Retinopathy
Author:
Affiliation:
1. Faculty of Engineering, University of Toyama
Publisher
Institute of Electronics, Information and Communications Engineers (IEICE)
Link
https://www.jstage.jst.go.jp/article/transinf/E107.D/9/E107.D_2023EDL8080/_pdf
Reference14 articles.
1. [1] M. Lotfy, J. Adeghate, H. Kalasz, J. Singh, and E. Adeghate, “Chronic complications of diabetes mellitus: A mini review,” Current Diabetes Reviews, vol.13, no.1, pp.3-10, 2017. 10.2174/1573399812666151016101622
2. [2] Y.S. Kanungo, B. Srinivasan, and S. Choudhary, “Detecting diabetic retinopathy using deep learning,” 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), pp.801-804, IEEE, 2017. 10.1109/RTEICT.2017.8256708
3. [3] M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.C. Chen, “MobileNetV2: Inverted residuals and linear bottlenecks,” Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.4510-4520, 2018.
4. [4] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.770-778, 2016.
5. [5] G. Huang, Z. Liu, L. van der Maaten, and K.Q. Weinberger, “Densely connected convolutional networks,” Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.4700-4708, July 2017.
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