Fundus Image Classification Using Wavelet Based Features in Detection of Glaucoma

Abstract

Glaucoma frequently called as the "noiseless hoodlum of sight". The main source of visual impairment worldwide beside Diabetic Retinopathy is Glaucoma. It is discernible by augmented pressure inside the eyeball result in optic disc harm and moderate however beyond any doubt loss of vision. As the renaissance of the worsened optic nerve filaments isn't suitable medicinally, glaucoma regularly goes covered up in its patients anticipating later stages. All around it is assessed that roughly 60.5 million individuals beyond 40 years old experience glaucoma in 2010. This number potentially will lift to 80 million by 2020. Late innovation in medical imaging provides effective quantitative imaging alternatives for the identification and supervision of glaucoma. Glaucoma order can be competently done utilizing surface highlights. The wavelet channels utilized as a part of this paper are daubechies, symlet3 which will expand the precision and execution of classification of glaucomatous pictures. These channels are inspected by utilizing a standard 2-D Discrete Wavelet Transform (DWT) which is utilized to separate features and examine changes. The separated features are sustained into the feed forward neural system classifier that classifies the normal images and abnormal glaucomatous images.