Eye fundus image-based diabetes and related disease classification method and equipment

A technology of fundus image and diabetes, which is applied in the field of classification of diabetes and related diseases, can solve problems such as inappropriateness, and achieve the effect of optimized classification performance and accurate classification results

Pending Publication Date: 2020-04-28
BEIJING AIRDOC TECH CO LTD +1
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This solution is suitable for identifying multiple diseases at the same time, such as detecting whether the fundus image has multiple irrelevant disease chara

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  • Eye fundus image-based diabetes and related disease classification method and equipment
  • Eye fundus image-based diabetes and related disease classification method and equipment
  • Eye fundus image-based diabetes and related disease classification method and equipment

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[0031] The technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present invention.

[0032] In addition, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as there is no conflict between them.

[0033] An embodiment of the present invention provides a method for constructing a classification model of diabetes and related diseases. This method involves a machine learning model, such as figure 1 The model shown includes a feature extraction network 11 and a plurality of output networks 12. The network mentioned...

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Abstract

The invention provides an eye fundus image-based diabetes and related disease classification method and equipment. The method comprises the steps that sample data are acquired, the sample data comprise fundus images and multiple types of information, and the multiple types of information are diabetes type information and at least one kind of diabetes related disease type information or at least two kinds of diabetes related disease type information; a machine learning model is trained by using a large amount of sample data, thus the evaluation results are output, wherein the evaluation resultat least comprises a classification result corresponding to the type information, the machine learning model comprises a feature extraction network and at least one output network, the feature extraction network is used for extracting feature information from the fundus image, and the at least one output network is used for outputting the evaluation result according to the feature information; andthe machine learning model at least adjusts own parameters according to the output evaluation result and the type information in the sample data.

Description

Technical field [0001] The invention relates to the field of medical image analysis, in particular to a method and equipment for classifying diabetes and related diseases based on fundus images. Background technique [0002] In recent years, machine learning technology has been widely used in the medical field. In particular, machine learning technology represented by deep learning has received widespread attention in the field of medical imaging. In terms of fundus image detection, deep learning technology can more accurately detect a certain feature of fundus images. For example, use a large number of fundus image samples of diabetic patients to train the deep learning model, and use the trained model to perform diabetes detection on the fundus image . [0003] The Chinese patent application number 201810387302.7 discloses a fundus image detection method based on machine learning. The method first detects the entire area of ​​the fundus image with a higher degree of feature dete...

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Application Information

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IPC IPC(8): G06T7/00G06N20/00
CPCG06T7/0012G06T2207/10004G06T2207/20081G06T2207/30041G06N20/00
Inventor 熊健皓王斌赵昕陈羽中和超张大磊
Owner BEIJING AIRDOC TECH CO LTD
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