Glaucoma fundus image recognition method based on transfer learning

Abstract

The invention discloses a glaucoma fundus image recognition method based on transfer learning, and the method comprises the following steps: 1, obtaining a glaucoma data set, and carrying out the preprocessing of a glaucoma fundus image; 2, constructing a convolutional neural network R-VGGNet; 3, loading the preprocessed training data set into an R-VGGNet convolutional neural network model to perform iterative training and feature extraction of the model; 4, inputting the extracted features into a softmax classifier to complete classification and recognition of glaucoma, and obtaining a finalrecognition model; 5, loading the test data set into the final identification model, and outputting corresponding classification accuracy. According to the method, a transfer learning thought is introduced, weight parameters obtained through training of a VGG16 network on an ImageNet data set are used for freezing the first 13 layers and releasing the weights of the second 3 layers, a glaucoma data set is used for training a full connection layer and a Softmax classifier, and feature extraction and classification are carried out after fine adjustment; the method meets the requirements of deeplearning, and effectively improves the recognition rate of the glaucoma fundus image.

Description

technical field [0001] The invention relates to eye disease image recognition, in particular to a glaucoma fundus image recognition method based on transfer learning. Background technique [0002] Glaucoma is an ophthalmic disease with chronic and progressive visual impairment. Its main pathological features are the apoptosis of retinal ganglion cells (RGC) and the loss of axons, which leads to gradual loss of vision or even blindness, which seriously threatens patients. health. Due to the high concealment of the disease, it is not easy to be discovered in the early stage of the disease, resulting in patients not being treated in time. Digital fundus image (DFI) is one of the main tools for detecting glaucoma at present, so it can be used for early detection of glaucoma to avoid further deterioration of the disease. However, fundus images are complex in structure, and it is time-consuming and labor-intensive to use artificial recognition methods, and it is difficult to obt...

AI Technical Summary

Problems solved by technology

[0004] In order to solve the defects of the prior art, the present invention adopts a transfer learning strategy to solve the problems of insufficient learning ability and low classification accuracy of small samples, and improve the recognition rate of glaucoma patients under small samples

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