Automated Detection of Age-Related Macular Degeneration through DWT Features and Deep Learning Approach


Authors : Sarmad Maqsood, Muhammad Tayyib, Musyyab Yousufi

Volume/Issue : Volume 4 - 2019, Issue 8 - August

Google Scholar : https://goo.gl/DF9R4u

Scribd : https://bit.ly/2ktStho

Age-related macular generation (AMD) is the major reason of sight loss for persons above 50 years of age. Automated algorithm makes possible in the early detection of AMD, by discovering variations in the blood vessel and arrays in the retina. AMD is steady damage of eye vision by rust of macula and general cause of permanent vision loss. The aim of this paper is to firstly detect the retinal disease AMD and to categorize the two kinds. In this paper, a Discrete Wavelet Transform (DWT) joined with LSDA for automated detection of AMD is proposed. The image is firstly preprocessed from chale. The extracted features are subjected to reduction through LSDA. The performance of classifier namely Deep Convolutional Neural Network (DCNN) is applied to detect the AMD disease and likened to automatically differentiate to wet and dry groups using classified LSDA factors. The results showed a classification of 97%.

Keywords : Age-Related Macular Degeneration, Fungus Images, Discrete Wavelet Transform, Deep Convolutional Neural Network.

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