基于改进BP神经网络的尿液中红白细胞识别算法

doi:10.11896/jsjkx.191100195

Abstract

Abstract: Analyzing the components of urine in the microscopic image such as red and white blood cells can help doctors evaluate patients with kidney and urinary diseases.According to the characteristics such as low contrast,fuzzy edge of red and white cells in the non-stained and unlabeled urine image,a recognition method based on improved BP neural network was proposed in this paper.Firstly,the method combines genetic algorithm with BP neural network to optimize the weights and thresholds,to solve the problems of local extremum in the training process and improve the recognition accuracy of the BP neural network.Secondly,it uses the method of momentum gradient descent to eliminate the oscillation of network in gradient descent,to accelerate the convergence of the network and improve the learning rate of BP neural network.Compared with basic BP neural network,the improved algorithm improves the recognition rate of red and white blood cells by 6.9% and 9.5%,and the recognition speed has increased by 19.3s and 42.1s.Compared with the CNN recognition algorithm,the improved algorithm improves the recognition rate of white blood cells by 1.7%.Compared with the SVM recognition algorithm,the improved algorithm improves the recognition rate of red and white blood cells by 12.9% and 12.7%.The results of verification test and control test show that the improved method can realize the recognition of red and white cells with higher accuracy and faster recognition speed.

Key words: BP neural network, Genetic algorithm, Gradient descent with momentum, Red and white cells, Urine formed element

CLC Number:

TP391.41

LIU Xiao-tong,WANG Wei,LI Ze-yu,SHEN Si-wan,JIANG Xiao-ming. Recognition Algorithm of Red and White Cells in Urine Based on Improved BP Neural Network[J].Computer Science, 2020, 47(2): 102-105.

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Recognition Algorithm of Red and White Cells in Urine Based on Improved BP Neural Network

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