Early Detection Glaucoma and Stargardt’s Disease Using Deep Learning Techniques

Retinal fundus images are used to discover many diseases. Several Machine learning algorithms designed identify the Glaucoma disease. But accuracy and time consumption performance were not improved. To address this problem Max Pool Convolution Neural Kuan Filtered Tobit Regressive Segmentation based Radial Basis Image Classifier (MPCNKFTRS-RBIC) Model is for detecting Stargardt’s disease by early period using higher minimal time. In MPCNKFTRS-RBIC Model, retinal image considered as an input which preprocessed in hidden layer 1 weighted adaptive filter. Then, given 2 extracting features like color, intensity, texture with accuracy. After these features, process performed 3 partitioning within more segments analyzing pixel extracted of image. segmented was output layer. The radial basis function analyzes testing region a particular class well training minimum consumption. Simulation dataset various metrics namely peak signal-to-noise ratio, time, error rate concerning several retina size.