Diabetic retina feature grading device in eye fundus image based on attention mechanism and feature fusion

Abstract

The invention discloses a diabetic retina feature grading device in an eye fundus image based on attention mechanism and feature fusion. The diabetic retina feature grading device comprises a featuredetection classification network module used for extracting first-grade diabetic retina features and second-grade diabetic retina features in the eye fundus image of an input sample, and outputting fine classification feature images extracted from the first-grade diabetic retina features and the second-grade diabetic retina features; an original image classification network module used for extracting third-grade diabetic retina features and fourth-grade diabetic retina features in the eye fundus image of the input sample, and outputting rough classification feature images extracted from the third-grade diabetic retina features and the fourth-grade diabetic retina features; an attention mechanism and feature fusion module for conducting feature fusion on the fine classification feature images output by the feature detection classification network module and the rough classification feature images output by the original image classification network module by adopting an attention mechanism, and outputting the prediction probability of the diabetic retina feature grade of the image of the input sample. The device ensures faster speed, and the classification evaluation index Kappa reaches 81.33%.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a device for grading sugar net features in fundus images based on attention mechanism and feature fusion. Background technique [0002] Most of the existing deep learning methods use the original image or the image after simple data enhancement to classify the features of the sugar network (diabetic retina) in the fundus image. First, the input image in the training data is passed into the neural network composed of a series of convolutional structures and fully connected structures, then the trained neural network parameters are saved, and finally the test images in the test set are processed using the trained neural network. Prediction, to get the predicted probabilities at the sugarnet feature level for each test image. In order to improve the receptive field of the neural network to the input image, the resolution of the input image will be sacrificed, w...

AI Technical Summary

Problems solved by technology

In order to improve the receptive field of the neural network to the input image, the resolution of the input image will be sacrificed, which will cause the neural network to lose some of the smaller feature information in the original input image, and only highlight the main features.

Therefore, the convolutional neural network generally has a better classification effect on the 3 and 4 levels of sugar network features that rely on more obvious features to distinguish, but the classification effect on 1 and 2 levels of sugar network features that rely on more subtle features to distinguish

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