Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (4): 247-253.doi: 10.11896/jsjkx.210200093

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Automatic Identification Algorithm of Blood Cell Image Based on Convolutional Neural Network

LI Guo-quan1,2, YAO Kai1,2, PANG Yu2   

  1. 1 School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
    2 Chongqing Key Laboratory of Photoelectronic Information Sensing and Transmitting Technology, Chongqing 400065, China
  • Received:2021-02-09 Revised:2021-05-26 Published:2022-04-01
  • About author:LI Guo-quan,born in 1980,Ph.D,professor,master supervisor.His main research interests include wireless communication transmission technology,heterogeneous wireless network transmission technology and medical signal processing and so on.
  • Supported by:
    This work was supported by the National Key Research and Development Program of China(2019YFC1511300) and Natural Science Foundation of Chongqing(cstc2019jcyj-msxmX0666,cstc2019jcyj-xfkxX0002).

PDF (PC)

1317

Abstract: A complete blood cell count is an important testing technique to evaluate overall health condition in medical diagnosis.In order to solve the problem that traditional blood cell counters and other devices are cumbersome and time-consuming for the artificial counting procedure of blood cells, a blood cell recognition algorithm based on convolutional neural networks is proposed, that is, three types of blood cells are automatically identified and counted based on Res2Net and YOLO object detection algorithm.The performance of the blood cell identification model is enhanced by incorporating Res2Net into the YOLO model to extract multiscale features represented by fine-grained and increase the range of receptive field in each network layer.After training and testing on an public blood smear image dataset, it can automatically identify and count red blood cells, white blood cells, and platelets, and the accuracy of identification reaches 93.44%, 96.09%, and 96.36%, respectively.Compared with other recognition models based on convolutional neural networks, the efficiency of blood detection can be significantly improved due to the high re-cognition accuracy and strong generalization.

Key words: Blood cell identification, Convolutional neural network, Res2Net, YOLO algorithm

CLC Number: 

  • TP391
[1] CRUZ D,JENNIFER C,VALIENTE L C,et al.Determination of blood components (WBCs,RBCs,and Platelets) count in microscopic images using image processing and analysis[C]//2017 IEEE 9th International Conference on Humanoid,Nanotechno-logy,Information Technology,Communication and Control,Environment and Management (HNICEM).Manila:IEEE Press,2017:1-7.
[2] HABIBZADEH M,KRZYAIJAK A.White blood cell differen-tial counts using convolutional neural networks for low resolution images[J].Proc. Int. Conf. on Artificial Intelligence and Soft Computing,2013,7895(8):263-274.
[3] ACHARJEE S,CHAKRABARTTY S M I,ALAM M I,et al.A semiautomated approach using GUI for the detection of red blood cells[C]//2016 International Conference on Electrical,Electronics and Optimization Techniques (ICEEOT).Chennai:IEEE Press,2016:525-529.
[4] SYED H S,ARIF I U,NUHMANUI H,et al.Extreme learning machine based microscopic red blood cells classification[J].Cluster Computing,2018,21(1):691-701.
[5] ACHARYA V,KUMAR P.Identification and red blood cell automated counting from blood smear images using computer-aided system[J].Medical & Biological Engineering & Computing,2018,56(3):483-489.
[6] SARRAFZADEH O,DEHNAVI A M,RABBANI H,et al.Circlet based framework for red blood cells segmentation and counting[C]//2015 IEEE Workshop on Signal Processing Systems (SiPS).Hangzhou,China:IEEE Press,2015:1-6.
[7] KAUR P,SHARMA V,GARG N.Platelet count using imageprocessing[C]//2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom).New Delhi,India:IEEE Press,2016:2574-2577.
[8] ISLAM M T,AOWAL M A,MINHAZ A T,et al.Abnormality detection and localization in chest X-rays using deep convolutional neural networks[EB/OL].(2017-9-27) [2020-11-30].https://arxiv.org/abs/1705.09850v3.
[9] AVENDI M R,KHERADVAR A,JAFARKHANI H.A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI[J].Medical Image Analysis,2016,30(6):108-119.
[10] GULSHAN V,PENG L,CORAM M,et al.Development andvalidation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs[J].J Am Med Assoc,2016,316(22):2402-2410.
[11] ZHAO J W,ZHANG M S,ZHOU Z H,et al.Automatic detection and classificationof leukocytes using convolutional neural networks[J].Medical & Biological Engineering & Computing,2017,55(8):1287-1301.
[12] HE K M,ZHANG X Y,REN S Q,et al.Deep Residual Learning for Image Recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Las Vegas,NV,USA:IEEE Press,2016:770-778.
[13] SZEGEDY C,VANHOUCKE V,IOFFE S,et al.Rethinking the Inception Architecture for Computer Vision[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Las Vegas,NV,USA:IEEE Press,2016:2818-2826.
[14] HABIBZADEH M,JANNESARI M,REZAEI Z,et al.Auto-matic white blood cell classification using pre-trained deep lear-ning models:ResNet and inception[C]//Tenth International Conference on Machine Vision (ICMV).Vienna,Austria:SPIE,2017:1-8.
[15] XU M,PAPAGEORGIOU D P,ABIDI S Z,et al.A deep convolutional neural network for classification of red blood cells in sickle cell anemia[J].PLoS Comput Biol,2017,13(10):1-27.
[16] MACAWILE M J,QUINONES V V,BALLADO A,et al.Whiteblood cell classification and counting using convolutional neural network[C]//2018 3rd International Conference on Control and Robotics Engineering (ICCRE).Nagoya,Japan:IEEE Press,2018:259-263.
[17] ACEVEDO A,ALFEREZ S,MERINO A,et al.Recognition of peripheral blood cell images using convolutional neural networks[J].Computer Methods and Programs in Biomedicine,2019,180(6):105020-105036.
[18] WANG Q,BI S,SUN M,et al.Deep learning approach to peripheral leukocyte recognition[J].PLOS ONE,2019,14(6):218-236.
[19] TOGACAR M,ERGEN B,COMERT Z.Classification of white blood cells using deep features obtained from Convolutional Neural Network models based on the combination of feature selection methods[J].Applied Soft Computing,2020,97(2):1568-1592.
[20] MALKAWI A,SALAMEH T,SHEYAB B,et al.White Blood Cells Classification Using Convolutional Neural Network Hybrid System[C]//2020 IEEE 5th Middle East and Africa Conference on Biomedical Engineering (MECBME).Amman,Jordan:IEEE Press,2020:1-5.
[21] SAHLOL A T,KOLLMANNSBERGER P,EWEES A.Efficient Classification of White Blood Cell Leukemia with Improved Swarm Optimization of Deep Features[J].Scientific Reports,2020,10(1):2536-2547.
[22] LONG F,PENG J J,SONG W T,et al.BloodCaps:A capsulenetwork based model for the multiclassification of human peripheral blood cells[J].Computer Methods and Programs in Biomedicine,2021,202(8):105972-105972.
[23] JIANG Z C,ZHAO L Q,LI S Y,et al.Real-time object detection method based on improved YOLOv4-tiny[EB/OL].(2020-11-09) [2020-11-30].https://arxiv.org/abs/2011.04244v1.
[24] GAO S H,CHENG M M,ZHAO K,et al.Res2Net:A New Multi-scale Backbone Architecture[EB/OL].(2019-09-01) [2020-11-30].https://arxiv.org/abs/1904.01169v2.
[25] BOCHKOVSKIY A.,WANG C Y,LIAO H Y.YOLOv4:Optimal Speed and Accuracy of Object Detection[EB/OL].(2020-04-23) [2020-11-30].https://arxiv.org/abs/2004.10934v1.
[26] REDMON J,FARHADI F.YOLO9000:Better,Faster,Stronger[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Honolulu,HI,USA:IEEE Press,2017:6517-6525.
[27] ZHENG Z H,PANG W,LIU W,et al.Distance-IoU Loss:Faster and Better Learning for Bounding Box Regression[EB/OL].(2019-11-19) [2020-11-30].https://arxiv.org/abs/1911.08287v1.
[28] SIMONYAN K,ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[C]//2015 International Conference on Learning Representations.San Diego,2015:1-14.
[29] HOWARD A G,ZHU M,KALENICHENKO B,et al.Mobilenets:efficient convolutional neural networksfor mobile vision applications[EB/OL].(2017-4-17) [2020-11-30].https://arxiv.org/abs/1704.04861.
[1] ZHOU Le-yuan, ZHANG Jian-hua, YUAN Tian-tian, CHEN Sheng-yong. Sequence-to-Sequence Chinese Continuous Sign Language Recognition and Translation with Multi- layer Attention Mechanism Fusion [J]. Computer Science, 2022, 49(9): 155-161.
[2] CHEN Yong-quan, JIANG Ying. Analysis Method of APP User Behavior Based on Convolutional Neural Network [J]. Computer Science, 2022, 49(8): 78-85.
[3] ZHU Cheng-zhang, HUANG Jia-er, XIAO Ya-long, WANG Han, ZOU Bei-ji. Deep Hash Retrieval Algorithm for Medical Images Based on Attention Mechanism [J]. Computer Science, 2022, 49(8): 113-119.
[4] DAI Zhao-xia, LI Jin-xin, ZHANG Xiang-dong, XU Xu, MEI Lin, ZHANG Liang. Super-resolution Reconstruction of MRI Based on DNGAN [J]. Computer Science, 2022, 49(7): 113-119.
[5] LIU Yue-hong, NIU Shao-hua, SHEN Xian-hao. Virtual Reality Video Intraframe Prediction Coding Based on Convolutional Neural Network [J]. Computer Science, 2022, 49(7): 127-131.
[6] XU Ming-ke, ZHANG Fan. Head Fusion:A Method to Improve Accuracy and Robustness of Speech Emotion Recognition [J]. Computer Science, 2022, 49(7): 132-141.
[7] WU Zi-bin, YAN Qiao. Projected Gradient Descent Algorithm with Momentum [J]. Computer Science, 2022, 49(6A): 178-183.
[8] ZHANG Jia-hao, LIU Feng, QI Jia-yin. Lightweight Micro-expression Recognition Architecture Based on Bottleneck Transformer [J]. Computer Science, 2022, 49(6A): 370-377.
[9] WANG Jian-ming, CHEN Xiang-yu, YANG Zi-zhong, SHI Chen-yang, ZHANG Yu-hang, QIAN Zheng-kun. Influence of Different Data Augmentation Methods on Model Recognition Accuracy [J]. Computer Science, 2022, 49(6A): 418-423.
[10] SUN Jie-qi, LI Ya-feng, ZHANG Wen-bo, LIU Peng-hui. Dual-field Feature Fusion Deep Convolutional Neural Network Based on Discrete Wavelet Transformation [J]. Computer Science, 2022, 49(6A): 434-440.
[11] YANG Yue, FENG Tao, LIANG Hong, YANG Yang. Image Arbitrary Style Transfer via Criss-cross Attention [J]. Computer Science, 2022, 49(6A): 345-352.
[12] YANG Jian-nan, ZHANG Fan. Classification Method for Small Crops Combining Dual Attention Mechanisms and Hierarchical Network Structure [J]. Computer Science, 2022, 49(6A): 353-357.
[13] ZHAO Zheng-peng, LI Jun-gang, PU Yuan-yuan. Low-light Image Enhancement Based on Retinex Theory by Convolutional Neural Network [J]. Computer Science, 2022, 49(6): 199-209.
[14] LIU Lin-yun, CHEN Kai-yan, LI Xiong-wei, ZHANG Yang, XIE Fang-fang. Overview of Side Channel Analysis Based on Convolutional Neural Network [J]. Computer Science, 2022, 49(5): 296-302.
[15] ZHANG Wen-xuan, WU Qin. Fine-grained Image Classification Based on Multi-branch Attention-augmentation [J]. Computer Science, 2022, 49(5): 105-112.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.