Abstract
The early and intermediate stages of age-related macular degeneration (AMD) are often asymptomatic and may lead to a neovascular form, which ends up causing blindness. The existing works on the detection of AMD make use of image processing and manual feature extraction methods. These methods detect drusen properties and use decision-making algorithms to obtain the desired results. The proposed work is a novel solution for the problem of AMD detection using a deep learning approach. The proposed method screens retinal images for detecting direct evidence of AMD. As deep learning model calculates features and learns on its own, there is less chance of neglecting any important feature which may happen in the existing methods. The proposed approach is applied to check for the presence of AMD on a dataset of healthy and diseased cases and a detection accuracy of 84% is obtained.
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Chakraborty, R., Pramanik, A. (2021). Deep Learning-Based Automated Detection of Age-Related Macular Degeneration from Retinal Fundus Images. In: Bhattacharjee, D., Kole, D.K., Dey, N., Basu, S., Plewczynski, D. (eds) Proceedings of International Conference on Frontiers in Computing and Systems. Advances in Intelligent Systems and Computing, vol 1255. Springer, Singapore. https://doi.org/10.1007/978-981-15-7834-2_41
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