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Method and device for processing eye fundus images based on deep learning

A fundus image and deep learning technology, applied in the field of computer vision, can solve the problems of inaccurate evaluation of the actual lesion degree, high computer performance requirements, complex and cumbersome processing and calculation, etc., to achieve simple and clear algorithms, low hardware requirements, and reduce leakage. reported effect

Inactive Publication Date: 2018-06-01
北京羽医甘蓝信息技术有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Existing schemes are accurate in judging whether diabetic retinopathy is present, but cannot accurately assess the actual degree of lesion
Third, the existing technical solutions have a large amount of calculation, and complex and tedious processing and calculations are required for each image, which requires high performance of the computer used by the terminal

Method used

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  • Method and device for processing eye fundus images based on deep learning
  • Method and device for processing eye fundus images based on deep learning
  • Method and device for processing eye fundus images based on deep learning

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Embodiment Construction

[0026] Exemplary implementations of the present invention are described below in conjunction with the accompanying drawings, which include various details of the implementations of the present invention to facilitate understanding, and they should be regarded as exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

[0027] The disadvantages of the prior art have already been explained in the background art. The neural network used in the deep learning solution adopted in the technical solution of the present invention has the characteristics of extracting high-level features of objects. Since the high-level feature information is a linear and nonlinear transformation of the u...

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Abstract

The invention provides an accurate and reliable method and device for processing eye fundus images based on deep learning in order to resolve problems in the prior art. The method for processing eye fundus images based on deep learning comprises the following steps: obtaining multiple original sample eye fundus images and corresponding lesion grade parameters; performing image pre-processing operation and image data enhancing operation on the multiple original sample eye fundus images in order to obtain multiple training sample eye fundus images; performing deep-network training on the multiple original sample eye fundus images and the corresponding lesion grade parameters in order to obtain an eye fundus lesion grade recognition model; and inputting test eye fundus images into the eye fundus lesion grade recognition model in order to obtain lesion grade recognition results.

Description

technical field [0001] The present invention relates to the technical field of computer vision, in particular to a method and device for processing fundus images based on deep learning. Background technique [0002] Diabetic retinopathy is one of the microvascular complications of diabetes. Chronic hyperglycemia damages retinal capillaries, causing leakage and blockage of capillaries, which can lead to vision loss and even blindness. In 2015, the number of people with diabetes in the world reached 415 million, of which 93 million patients were affected by different degrees of diabetic retinopathy. With prompt diagnosis and treatment, vision loss in people with diabetic retinopathy can often be slowed or even reversed. However, because the early symptoms of the disease are not obvious, most patients fail to see a doctor in time and miss the best time for treatment. [0003] At present, the clinical detection of diabetic retinopathy still needs to be completed by profession...

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Application Information

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IPC IPC(8): A61B3/12A61B3/14G06T7/00
CPCA61B3/12A61B3/0016A61B3/14G06T7/0012G06T2207/20081G06T2207/20084G06T2207/30041
Inventor 白海龙丁鹏王璋徐通
Owner 北京羽医甘蓝信息技术有限公司
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