基于改进BP神经网络的尿液中红白细胞识别算法  

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

作　　者：刘晓彤 王伟[1,2] 李泽禹 沈思婉 姜小明 LIU Xiao-tong;WANG Wei;LI Ze-yu;SHEN Si-wan;JIANG Xiao-ming(Research Centre of Biomedical Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China;Chongqing Engineering Research Center of Medical Electronics and Information Technology,Chongqing 400065,China)

机构地区：[1]重庆邮电大学生物医学工程研究中心,重庆400065 [2]重庆市医用电子与信息技术工程研究中心,重庆400065

出　　处：《计算机科学》2020年第2期102-105,共4页Computer Science

基　　金：国家自然科学基金(61801069);重庆市教委科学技术研究项目(KJ1704073)~~

摘　　要：对显微图像中的尿液有形成分包括红白细胞等进行分析,可以帮助医生对有肾脏和泌尿系统疾病的患者进行评估。针对无染色、无标记的尿液图像中红白细胞存在对比度低、边缘模糊等问题,提出一种基于改进BP神经网络的识别方法。首先,将遗传算法引入BP神经网络对网络权值和阈值进行优化,解决训练过程中网络容易陷入局部极值等问题,提高BP神经网络的识别精度;其次,使用动量梯度下降法消除网络在梯度下降中产生的摆动,加快网络的收敛,提高BP神经网络的学习速度。与基础BP神经网络相比,改进方法对红白细胞的识别准确度分别提高了6.9%和9.5%,且识别时间分别缩短了19.3 s和42.1 s;与CNN识别算法相比,改进算法对白细胞的识别准确度提高了1.7%;与SVM识别算法相比,改进算法对红白细胞的识别准确度分别提高了12.9%和12.7%。验证实验和对照实验的结果表明,改进方法能以较高的准确率和较快的速度实现红白细胞的识别。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 19.3 s and 42.1 s.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.

关 键 词：尿液有形成分 红白细胞 遗传算法 BP神经网络 动量梯度下降法 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP183[自动化与计算机技术—计算机科学与技术]