Cell recognition method and device based on stochastic forest classification model

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 ...

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, reduced classification accuracy of a single decision tree, and insufficient generalization ability of the entire classifier.

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