Classification of Retinal Images Using Convolutional Neural Network
|© 2020 by IJCTT Journal|
|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
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.
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pooling layer, ReLU layer, Matlab.