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

A depth network detection method for diabetic retinopathy based on a genetic fuzzy tree, comprising:

• S1:
• enhancing a retina image, wherein a lesion area of the retina image and a normal area around the lesion area show visually obvious different features and are formed as different image areas, and an interested lesion area of the retina image is widened and an uninterested background area of the retina image is compressed by using an image enhancement Gamma correction method;
• S2, building a network model U-net, wherein the network model U-net is divided into a compression path and an expansion path, and comprises four down-samplings and four up-samplings, two convolutions and one maximum pooling are performed prior to each sampling, the retina image is subjected to feature compression by four down-samplings in the compression path, an effective feature layer obtained by a last down-sampling is subjected to four up-samplings in the expansion path, corresponding feature layers in the down-sampling are connected, retinal feature map is normalized by 1*1 convolution, and the built model is trained with enhanced image data to obtain an image segmentation model;
• S3, fuzzing a vascular image segmented by the image segmentation model, calculating a membership degree of an attribute in the image and a fuzzy information gain, which together with a real diagnosis result are used for training to obtain a decision rule of each branch node of a fuzzy decision tree and a result set of leaf nodes for classification and prediction;
• S4:
• encoding each node of the decision tree, constructing a fitness function to measure pros and cons of a fuzzy tree model from two aspects of accuracy and complexity, wherein an accurate function E indicates an accuracy of the model, and as E decreases, the accuracy increases, and a number M of leaf nodes of the tree reflects a complexity of the model, as M decreases, the complexity of the model decreases, and a fitness function s(T) is defined as follows:
• s
• ⁡
• (
• T
• )
• =
• W
• E
• ×
• 1
• E
• +
• W
• M
• ×
• 1
• M
• ,
• (
• 1
• )
• where WE and WM are weights of the accuracy E and the number M of leaf nodes, respectively, WE+WM=1, and s(T) represents a fitness of the tree T;
• combining and optimizing a plurality of decision trees based on a genetic algorithm;
• S5:
• dynamically adjusting a penalty term in a loss function according to a distance between a sample class and a true value by introducing an accuracy, so as to further improve a classification accuracy.