Diabetic retinopathy classification device based on local lesion characteristics

Abstract

The invention belongs to the field of medical image classification, and relates to deep learning, in particular to a diabetic retinopathy classification device based on local lesion features, which isused for solving the problems of poor classification effect of a depth model on a fundus image, lack of interpretability of a result and the like. According to the method, the global features and thelocal lesion information of the fundus image are extracted respectively, so that the information in the image with serious lesion degree can be fully utilized, and the problem of poor application effect of deep learning in the field of sugar network lesion classification caused by insufficient and unbalanced data volume is solved; in addition, in the local focus information extraction process, the labeling result of the local focus information in the fundus image is output, the problem that the interpretability degree of a deep learning model result is low is solved, and the auxiliary effecton disease diagnosis of ophthalmologists is improved.

Description

technical field [0001] The invention belongs to the field of medical image classification, relates to deep learning, and specifically relates to a diabetic retinopathy classification device based on local lesion features. Background technique [0002] Diabetic retinopathy, generally referred to as diabetic retinopathy, is a kind of vascular disease, which is a relatively common complication of diabetes. It will cause serious problems such as rupture of human blood vessels, ischemia, and blindness in both eyes. Examination of retinal fundus images of patients is the main method for diagnosis of diabetic reticulopathy, while the traditional method of diagnosing diabetic reticulopathy relies on ophthalmologists to examine retinal fundus images of patients. This method has high accuracy but is time-consuming and labor-intensive, and is prone to delay treat. [0003] With the improvement of living standards, people's health awareness is gradually enhanced, and the development o...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to address the above-mentioned defects and provide a device for classifying diabetic retinopathy based on local lesion features based on deep learning technology, aiming to solve the problem of deep model in the fundus Problems such as poor classification effect on images and lack of interpretation of results

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