Diabetic retinopathy classification device based on local lesion characteristics

A technology of diabetic retina and classification device, applied in the field of diabetic retinopathy classification device and deep learning, can solve problems such as poor classification effect of deep model and lack of results, and achieve the effect of solving poor application effect and improving auxiliary effect.

Active Publication Date: 2020-01-03
UNIV OF ELECTRONIC SCI & TECH OF CHINA
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  • Abstract
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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 base

Method used

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  • Diabetic retinopathy classification device based on local lesion characteristics
  • Diabetic retinopathy classification device based on local lesion characteristics
  • Diabetic retinopathy classification device based on local lesion characteristics

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Embodiment Construction

[0038] The present invention will be further described below in conjunction with accompanying drawing and embodiment:

[0039] This embodiment provides a device for classifying diabetic retinopathy based on local lesion characteristics, the device comprising: an acquisition device for collecting retinal fundus images, and a computer for receiving the retinal fundus images; the computer is programmed to perform like figure 1 The steps shown include a total of 5 steps, namely: preprocessing, global feature extraction, local lesion feature extraction, feature fusion and lesion degree prediction; among them, preprocessing is the basic step of subsequent training and prediction, global feature extraction and local lesion Feature extraction is to obtain the feature information of the fundus image from the global and local respectively, and the feature fusion combines the global and local feature information, and finally obtains the prediction result of diabetic retinopathy through t...

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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...

Claims

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Application Information

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IPC IPC(8): G06T7/13G06T7/60G06T5/00G06K9/62G16H30/20G16H50/20
CPCG06T7/13G06T7/60G06T5/001G16H30/20G16H50/20G06T2207/20081G06T2207/30041G06T2207/30096G06F18/24G06F18/214
Inventor 段贵多朱大勇赵太银任亚洲刘江明
Owner UNIV OF ELECTRONIC SCI & TECH OF CHINA
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