An Automated Early Detection of Glaucoma using Support Vector Machine Based Visual Geometry Group 19 (VGG-19) Convolutional Neural Network

Author:

Raja J.,Shanmugam P.,Pitchai R.

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Computer Science Applications

Reference21 articles.

1. Kolar, Radim. (2008). Detection of glaucomatous eye via color fundus images using fractal dimensions. Radio Engineering, 17(3), 109–114.

2. Liu et al., (2008). An automatic cup-to-disc ratio measurement system for glaucoma analysis using level-set image processing. In 13th International Conference on Biomedical Engineering (ICBME)

3. Walter, T., & Klein, J.-C. (2001). Segmentation of color fundus images of the human retina: detection of the optic disc and the vascular tree using morphological techniques. International Symposium Medical. Data and Analysis, 2199, 282–287.

4. Bock, R., Meier, J., Nyl, L. G., Hornegger, J., & Michelson, G. (2010). Glaucoma risk index: Automated glaucoma detection from color fundus images. Medical Image Analysis, 4, 471–481.

5. Naz, Sobia, & Rao, Sheela N. (2014). Glaucoma Detection in Color Fundus Images Using Cup to Disc Ratio. The International Journal of Engineering and Sciences, 3(6), 51–58.

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