Depth network detection method for diabetic retinopathy based on genetic fuzzy tree

Abstract

A depth network detection method for diabetic retinopathy based on a genetic fuzzy tree. The method includes: first, enhancing a retina image to widen a lesion area, and compress a normal area; next, building a network model U-net to accurately segment images of retinal blood vessels and blood vessel tips; subsequently, performing training according to the vascular images segmented by the model and real diagnosis results, so as to construct an interpretable fuzzy decision tree; then, encoding weights of the decision tree and constructing a fitness function, and a plurality of decision trees being combined and optimized based on a genetic algorithm; and finally, introducing an accuracy index to dynamically adjust a penalty term in a loss function.