Biological cell counting method based on convolutional neural network and feature fusion

Abstract

The invention discloses a biological cell counting method based on a convolutional neural network and feature fusion. The method is suitable for realizing the cell counting in a biological cell microscopic image with a larger number and more impurities. The method comprises the following steps of preprocessing a biological cell microscopic image data set to obtain a training set and a test set; constructing a biological cell counting model based on the convolutional neural network and the deep and shallow layer feature fusion; training the convolutional neural network model, and obtaining an optimized model weight parameter through a propagation algorithm and parameter updating by using the pre-processed training set and the constructed convolutional neural network model; and testing the convolutional neural network model, testing the model by using the preprocessed test set and the obtained weight parameters of the optimal network model to obtain an output biological cell density mapand the cell estimation quantity, and evaluating. The method can improve the feature extraction effect of the biological cells and improve the cell counting accuracy.

Description

technical field [0001] The invention relates to the technical fields of computer vision and deep learning, in particular to a deep and shallow feature fusion cell counting method based on a convolutional neural network. Background technique [0002] In biomedical cell research, technicians have more and more significant needs for the research and analysis of biological cell microscopic images. In order to achieve the research purpose, it is necessary to process and analyze cells through various computer image technologies. Processing includes cell detection, segmentation, counting, and more. Among them, cell counting technology has been more and more widely used. In medicine, many diseases and drug research need to know the number of certain specific cells: on the one hand, it can be based on the number of target cells in the tissue microenvironment Judging the disease condition; on the other hand, in drug screening, it can be judged whether the test drug has a significant ...

AI Technical Summary

Problems solved by technology

This detection-based counting method can have high accuracy after training, but it is limited to images with rich cell features and a small number of cells, and the efficiency of detecting one by one is low, so the researchers established for the cell counting task The purpose of the regression model is to learn the direct mapping between the features in the image and the number of cells. In the training phase, the cell map and annotation information in the training set are used to obtain the mapping relationship between the cells and the number. In the testing phase, the cell number is directly obtained according to the input image. quantity estimate

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