Cell recognition method and device based on stochastic forest classification model

A random forest classification and model technology, applied in the field of image recognition algorithms and machine learning, can solve the problems of feature redundancy, reduced classification accuracy of a single decision tree, and insufficient generalization ability of classifiers, so as to improve classification accuracy and solve feature problems. redundant effect

Pending Publication Date: 2019-01-04
深圳辉煌耀强科技有限公司
View PDF5 Cites 8 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, if the size of the feature subset is not selected properly, there may be impacts such as feature redundancy, red

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
  • Cell recognition method and device based on stochastic forest classification model
  • Cell recognition method and device based on stochastic forest classification model
  • Cell recognition method and device based on stochastic forest classification model

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0051] Based on the following detailed description of the specific embodiments of the present application in conjunction with the accompanying drawings, those skilled in the art will better understand the above and other objectives, advantages and features of the present application.

[0052] As a single classifier, the decision tree has high classification efficiency, but its classification results often show a local optimal solution instead of a global optimal solution; during the training process of the decision tree, overfitting is prone to occur. The random forest algorithm is composed of a series of mutually independent decision trees, and each decision tree constitutes the smallest composition of the entire random forest algorithm. Its expression can be written as R={h(x,θ k ),k=1,2,...K}, where {θ k } Is the randomness vector, subject to independent and identical distribution, and K is the number of individual decision trees in the entire classifier. When the random fores...

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

A method and apparatus for cell recognition based on a stochastic forest classification model are disclosed. The method comprises the following steps of: training a random forest classification modelby using an original cell image sample set, testing the random forest classification model to obtain an optimal sample accuracy rate; taking the stochastic forest classification model corresponding tothe optimal sample accuracy rate as a fitness value calculation function, wherein the optimal artificial fish is obtained by artificial fish swarm algorithm, and the initial values of the preset number of trees and the number parameter of the feature subset of the stochastic forest classification model are updated, and the above steps are repeated until the optimal eigenvalue pair is no longer changed. The cells in the image to be detected are classified using a stochastic forest classification model corresponding to the optimal eigenvalue. The present application utilizes artificial fish swarm algorithm for feature selection of a random forest classifier, and solves the problem of feature redundancy in the model and insufficient generalization ability of the whole classifier.

Description

Technical field [0001] This application relates to the field of image recognition algorithms and machine learning, and in particular to a method and device for identifying cervical epithelial cells based on a random forest model. Background technique [0002] Regarding cell recognition, commonly used classifiers in the prior art include: decision trees, random forests, etc.; commonly used algorithms for feature selection include: artificial fish school algorithm (AFSA), etc. Among them, the size of the random forest determines the diversity of the sample subspace, but it is not appropriate for its size to be too large or too small. At the same time, in order to increase the diversity of feature subspaces, features are randomly selected from the total features for learning from a single decision tree. However, if the size of the feature subset is not selected properly, there may be effects such as feature redundancy, reduced classification accuracy of a single decision tree, and ...

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
IPC IPC(8): G06K9/62G06N3/00
CPCG06N3/006G06F18/241G06F18/214
Inventor 郏东耀李玉娟曾强庄重
Owner 深圳辉煌耀强科技有限公司
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap