Computer Science ›› 2020, Vol. 47 ›› Issue (2): 102-105.doi: 10.11896/jsjkx.191100195

Special Issue: Medical Imaging

• Computer Graphics & Multimedia • Previous Articles     Next Articles

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

LIU Xiao-tong,WANG Wei,LI Ze-yu,SHEN Si-wan,JIANG Xiao-ming   

  1. (Research Centre of Biomedical Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China)1;
    (Chongqing Engineering Research Center of Medical Electronics and Information Technology,Chongqing 400065,China)2
  • Received:2019-08-05 Online:2020-02-15 Published:2020-03-18
  • About author:LIU Xiao-tong,born in 1993,postgra-duate,is member of China Computer Federation (CCF).Her main research interest include medical image proces-sing;WANG Wei,born in 1977,associate professor.His main research interests include digital medical instruments and medical image processing.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61801069) and Chongqing Education Commission Science and Technology Research Project (KJ1704073).

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
[1]LIANG Y X,KANG R,LIAN C Y,et al.An end-to-end system for automatic urinary particle recognition with convolutional neural network[J].Journal of Medical Systems,2018,42(9):165.
[2]SOMMER C,GERLICH D W.Machine learning in cell biology-teaching computers to recognize phenotypes[J].Journal of Cell Science,2013,126(24):5529-5539.
[3]TU L L.Urinary sediment cell classification recognition system based on SVM algorithm research[D].Wuhan:Wuhan University of Technology,2014.
[4]MOLINA-CABELLO M A,LÓPEZ-RUBIO E,LUQUE-BAE-NA R M,et al.Blood cell classification using the hough transform and convolutional neural networks[M]∥Advances in Intelligent Systems and Computing.Cham:Springer International Publishing,2018:669-678.
[5]LIU Y C,RICHARD D,ZHANG Y C.Research on Pan-real-time Problem of Medical Detection Based on BPNNs Recognition Algorithm[J].Computer Science,2018,45(6):307-313.
[6]LI B,HAN C,BAI B.Hybrid approach for human posture recognition using anthropometry and BP neural network based on Kinect V2[J].EURASIP Journal on Image and Video Proces-sing,2019(1):1-15.
[7]WU Z P,ZHAO Y L,LUO Z L,et al.License plate recognition technology based on PSO-BP neural network[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2017,56(1):46-52.
[8]LI D J,LI Y Y,LI J X,et al.Gesture recognition based on BP neural network improved by chaotic genetic algorithm[J].International Journal of Automation and Computing,2018,15(3):267-276.
[9]CHENG H X,LIU J L.Application of BP neural network optimized by genetic algorithm in handwritten numeral recognition[J].Electronic Measurement Technology,2019,42(9):89-92.
[10]PAN J H,WANG Y H,WU W.Physical quantity regression method based on optimized BP neural network[J].Computer Science,2018,45(12):170-176.
[11]YOGANAND A V,KAVIDA A C,RUKMANIDEVI.Face detection approach from video with the aid of KPCM and improved neural network classifier[J].Multimedia Tools and Applications,2018,77(24):31763-31785.
[12]ELSALAMONY H A.Detection of anaemia disease in human red blood cells using cell signature,neural networks and SVM[J].Multimedia Tools and Applications,2018,77(12):15047-15074.
[13]XIAO M H,MA Y,FENG Z X,et al.Rice blast recognition based on principal component analysis and neural network[J].Computers and Electronics in Agriculture,2018,154:482-490.
[14]DAI K K,ZHAO J W,CAO F L.A novel algorithm of extended neural networks for image recognition[J].Engineering Applications of Artificial Intelligence,2015,42:57-66.
[15]BADI H,HAMZA A,HASAN S.New method for optimization of static hand gesture recognition[C]∥2017 Intelligent Systems Conference (IntelliSys).IEEE,2017:542-544.
[16]WANG J C,YU Y,YANG K,et al.Brain tumor segmentation of MRI based on BP neural network[J].Journal of Biomedical Engineering Research,2016,35(4):290-293.
[17]SUN Y,XUE B,ZHANG M,et al.Automatically Designing CNN Architectures Using Genetic Algorithm for Image Classification[J].arXiv:1808.03818,2018.
[18]YAN X,LI S Y,ZHANG Z.Application of BP neural network based on genetic algorithms in prediction model of City water consumption[J].Computer Science,2016,43(S2):547-550.
[19]LI Y M.The research of the urinary sediment images automatic recognition algorithm[D].Chongqing:Chongqing University,2007.
[20]ADEM K.Exudate detection for diabetic retinopathy with circular Hough transformation and convolutional neural networks[J].Expert Systems With Applications,2018,114:289-295.
[21]MU N,XU X,ZHANG X L,et al.Salient object detection using a covariance-based CNN model in low-contrast images[J].Neural Computing and Applications,2018,29(8):181-192.
[22]OU X F,YAN P C,HE W,et al.Adaptive GMM and BP neural network hybrid method for moving objects detection in complex scenes[J].International Journal of Pattern Recognition and Artificial Intelligence,2019,33(2):1950004.
[23]ZHU J M,HU L Y.Comparative Analysis of RMB Exchange Rate Forecast Based on ARIMA and BP Neural Network-Take the exchange rate of US dollar to RMB as an example.Journal of Chongqing University of Technology(Natural Science),2019,33(5):207-212.
[24]TIAN Z S,CUI Y Q.Attitude measurement fusion algorithm in GPS/SINS based on BP neural-network.Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition),2014,26(4):478-482.
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