Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Retinal fundus image classification method based on improved CNN model

A picture classification and retina technology, applied in the field of image processing, can solve the problems of unrepresentative single data set, limited classification method, poor picture classification accuracy, etc., and achieve the effect of high reliability, good accuracy and less resource occupation

Active Publication Date: 2020-05-12
HUNAN UNIV
View PDF9 Cites 3 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the current classification technology generally uses a small single data set to train and classify the classifier; however, a single data set is not representative, and the classifier uses a relatively primitive classifier, so it makes The classification accuracy of pictures is poor, which limits the application of classification methods

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Retinal fundus image classification method based on improved CNN model
  • Retinal fundus image classification method based on improved CNN model
  • Retinal fundus image classification method based on improved CNN model

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0044] Such as figure 1 Shown is the schematic flow chart of the method of the present invention method: the method for classifying retinal fundus pictures based on the improved CNN model provided by the present invention comprises the following steps:

[0045] S1. Classify and mark the acquired training pictures; specifically, divide the training pictures into abnormal and normal categories, which are represented by disease, disease=1 means abnormal, disease=0 means normal; then mark the abnormal pictures again as abnormal Degree, represented by level, the value of level is 0, 1, 2, 3 and 4, which are used to indicate the lightest abnormality to the heaviest abnormality in turn;

[0046] S2. Perform image preprocessing on the training picture obtained in step S1; specifically, the following steps are used for preprocessing:

[0047] A. Obtain the eyeball radius according to the pixel value of each picture;

[0048] B. Cut out the smallest square picture containing the eyeba...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a retinal fundus image classification method based on an improved CNN model. The method comprises the following steps: classifying and marking acquired training images; performing image preprocessing on the training picture; establishing an improved CNN model; training the improved CNN model by adopting the training pictures to obtain a picture classifier; and classifying the retinal fundus images to be detected by adopting an image classifier to obtain a final classification result. The improved CNN model and the improved CNN classification method based on multiple tasks are excellent in performance, higher in efficiency, less in occupied resources, high in reliability and good in accuracy.

Description

technical field [0001] The invention belongs to the field of image processing, and in particular relates to a method for classifying retinal fundus pictures based on an improved CNN model. Background technique [0002] With the development of economy and technology and the improvement of people's living standards, people are paying more and more attention to their own health. [0003] Retinal fundus pictures can reflect people's health status to a certain extent. Therefore, analyzing and classifying the color fundus pictures of the detected person has become an auxiliary detection method for retinopathy (such as diabetic retinopathy). [0004] At present, there have been a large number of studies on the classification of color fundus images of diabetic retinopathy. However, the current classification technology generally uses a smaller single data set to train and classify the classifier; however, a single data set is not representative, and the classifier uses a relativel...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06N3/08
CPCG06N3/084G06V40/197
Inventor 荣辉桂奚子为蒋洪波王敏火生旭
Owner HUNAN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com