Fundus image classification method and device of retinopathy

Abstract

The present invention provides a fundus image classification method and device of retinopathy. The method comprises a step of obtaining the fundus image of an eye detection patient, a step of preprocessing the fundus image and obtaining a corresponding grey fundus image, a step of extracting a preset image characteristic in the grey fundus image, a step of analyzing the preset image characteristic according to the diabetic retinopathy classification model of the fundus image, and obtaining the classification result of the corresponding fundus image, a step of extracting the preset image characteristic in the preprocessed grey fundus image, and carrying out prediction classification on the above preset image characteristic through the diabetic retinopathy classification model established according to a support vector machine and a deep neural network so as to obtain a final classification result. The artificial participation is not needed in the whole process, the workload of doctors is reduced, the high standard requirement of the medical qualification level of the doctor is reduced, the accuracy of classification is improved, and the method and the device have great significance for specific applications and medical research.

Description

technical field [0001] The present invention relates to the technical field of image processing, and in particular, to a fundus image classification method and device for retinopathy. Background technique [0002] Eye disease patients need to take their fundus photos before undergoing ophthalmological examination. Then, the ophthalmologist manually checks and analyzes the clarity of the main parts of the fundus in the fundus photos, and thus preliminarily determines whether the patient suffers from DR (Diabetic Retinopathy). , diabetic retinopathy) and the severity of DR. [0003] Specifically, the ophthalmologist obtains a large number of fundus photo images of the patient and checks the above-mentioned large number of fundus photo images, and then analyzes the above-mentioned fundus photo images based on personal experience to classify the above-mentioned fundus photo images, and finally according to the above classification situation Determine whether the patient suffers...

AI Technical Summary

Problems solved by technology

[0004] However, in the above-mentioned diagnostic method, it is necessary to manually classify and process a large number of fundus photo images, which makes the doctor's workload larger and the above-mentioned diagnostic method requires a higher level of medical resources for the doctor.

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