Cell classification method and device, computer equipment and storage medium

Abstract

The invention relates to a cell classification method and device, computer equipment and a storage medium. The method comprises the following steps of: obtaining a to-be-analyzed image and inputting the to-be-analyzed image into a trained target detection model, obtaining position information and initial classification information of each target cell in the to-be-analyzed image; wherein the labeling information comprises cell position information and cell type information; segmenting a to-be-analyzed image according to the position information of the target cell; obtaining a plurality of target cell images, extracting a cell feature vector of the target cell image according to a preset feature extraction network; inputting the cell feature vectors into a trained SVM model; and obtaining probability data of the target cell belonging to each preset category in the target cell image, marking the preset category with the maximum probability data as secondary classification information of the target cell, and marking the secondary classification information as a classification result of the target cell when the initial classification information is the same as the secondary classification information. By adopting the method, the cell category can be accurately determined, and the cell recognition accuracy is improved.

Description

technical field [0001] The present application relates to the technical field of image recognition, in particular to a cell classification method, device, computer equipment and storage medium. Background technique [0002] With the development of medical science and technology, cell identification technology has appeared. Cell identification includes screening of cell types. Through the screening of cell types, diseased cells can be found in time, so as to effectively deal with diseases. The traditional cell identification adopts the method of manual identification. The pathologist moves the pathological slice and then scans the entire slice with naked eyes to identify the type of each cell in the slice. [0003] However, due to the high similarity between diseased cells and normal cells in the early stage, the traditional cell identification method has the problem of low identification accuracy. Contents of the invention [0004] Based on this, it is necessary to provid...

AI Technical Summary

Problems solved by technology

[0003] However, due to the high similarity between diseased cells and normal cells in the early stage, the traditional cell identification method has the problem of low recognition accuracy

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