Method and device for classifing densities of cells, electronic device using method, and storage medium

An electronic device comprising:

• a storage device;
• at least one processor;
• and
• the storage device storing one or more programs, which when executed by the at least one processor, cause the at least one processor to:
• obtain positional information of all central points of all groups of first encoding features generated when training a model of convolutional neural network and ranges of densities of images of biological cells represented by different central points;
• each of the groups of the first encoding features comprising a central point and corresponding to a plurality of images of the biological cells within a range of densities;
• input a test image of the biological cells into a trained model of the convolutional neural network to encode the test image of the biological cells, to obtain a second encoding feature;
• determine a central point nearest to the second encoding feature according to the positional information of all the central points of all the groups of the first encoding features;
• determine a range of densities of the test image of the biological cells according to the ranges of densities of the images of the biological cells represented by different central points and the central point nearest to the second encoding feature.

A non-transitory storage medium storing a set of commands, when the commands being executed by at least one processor of an electronic device, causing the at least one processor to:

• obtain positional information of all central points of all groups of first encoding features generated when training a model of convolutional neural network and ranges of densities of images of biological cells represented by different central points;
• each of the groups of the first encoding features comprising a central point and corresponding to a plurality of images of the biological cells within a range of densities;
• input a test image of the biological cells into a trained model of the convolutional neural network to encode the test image of the biological cells, to obtain a second encoding feature;
• determine a central point nearest to the second encoding feature according to the positional information of all the central points of all the groups of the first encoding features;
• determine a range of densities of the test image of the biological cells according to the ranges of densities of the images of the biological cells represented by different central points and the central point nearest to the second encoding feature.