SEGMENTATION AND IDENTIFICATION OF DISEASES IN RETINAL BLOOD VESSEL USING WIDE AND DEEP NEURAL NETWORK SYSTEM

A new supervised method for vessel segmentation in retinal images is studied here. Proposed remolds the task of as a problem cross-modality data transformation from image to map. wide and deep neural network with strong induction ability proposed model transformation, an efficient training strategy presented. Instead single label center pixel, can output map all pixels given patch. Our approach outperforms reported state-of-the-art methods terms sensitivity, specificity accuracy. The result cross-training evaluation indicates its robustness set. needs no artificially designed feature preprocessing step, reducing impact subjective factors. has potential application diagnosis ophthalmologic diseases, it may provide new, general, high- performance computing framework segmentation. Key Words- method, cross-modality, evaluation.