IJBTT

Classification of Retinal Images Using Convolutional Neural Network
	© 2020 by IJCTT Journal
	Volume-68 Issue-9
	Year of Publication : 2020
	Authors : MAMTHA.G, S.MOHAN, Dr.V.JAYARAJ, J.VINOTH KUMAR
	DOI :  10.14445/22312803/IJCTT-V68I9P105

How to Cite?

MAMTHA.G, S.MOHAN, Dr.V.JAYARAJ, J.VINOTH KUMAR, "Classification of Retinal Images Using Convolutional Neural Network," International Journal of Computer Trends and Technology, vol. 68, no. 9, pp. 31-36, 2020. Crossref, 10.14445/22312803/IJCTT-V68I9P105

Abstract
In the early days, many computer vision algorithms approached this problem from signal processing based on the assumption that the vessels follow particular patterns. Fluorescein angiography (FA) is an established approach to visualize, verify, and understand the effect of retinal disorders. The proposed cross-modality technique, however, builds in invariance to contrast and publicity. The use of parametric chamfer alignment for our registration manner is also well-matched and advantageous within the proposed automatic method for education data technology for 2 motives matching function factors is pretty difficult for the massive one-of-akind CF and FA modalities. By means of the use of parametric chamfer alignment, we put off this undertaking. Second, the chamfer alignment components` uneven nature lets us gain a particular alignment using preliminary vessel detection with a low fake wonderful fee, even if the corresponding proper fine price is also low.

Reference
[1] Esteva,alDermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (2017).
[2] Gulshan, V. et al. “Development and validation of the deep learning algorithm to detect diabetic retinopathy in retinal fundus photographs”. JAMA316, 2402–2410 (2016).
[3] Angermueller, C.,Pärnamaa,T.,Parts, L. & “Stegle, Of Deep learning for computational biology”. Mol. Syst. Biol (2016).
[4] J. Schmidhuber, “Deep learning in neural networks: An overview, "Neural Networks,vol.61, pp. 85–117, 2015.
[5] S.Roychowdhury, D.D.Koozekanani, and K. K. Parhi"Blood vessel segmentation of fundus images by major vessel extraction and sub-image classification Biomedical and Health Informatics”, IEEE Journal of, vol. 19, no. 3, pp. 1118–1128, 2015.
[6] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J. & Wojna, “Rethinking the inception architecture for computer vision”.Preprint at (2015).
[7] S. Roychowdhury, D. D. Koozekanani, and K. K. Parhi, “Iterativevessel segmentation of fundus images,” Biomedical Engineering, IEEE Transactions on, vol. 62, no. 7, pp. 1738–1749, 2015.
[8] Bahdanau, D., Cho, K. & Bengio, Y. “Neural machine translation by jointly learning to align and translate”. Preprint at (2014).
[9] M. M. Fraz, A. R. Rudnicka, C. G. Owen, and S. A. Barman, “Delineation of blood vessels in pediatric retinal images using decision trees-based ensemble classification,” International Journal of computer-assisted radiology and surgery vol. 9, no. 5, pp. 795–811, 2014.
[10] Bahdanau, D., Cho, K. & Bengio, Y. “Neural machine translation by jointly learning to align and translate”. Preprint at (2014)

Keywords
pooling layer, ReLU layer, Matlab.