Microscopic image identification for small-sample Chinese medicinal materials powder based on deep learning

doi:10.11772/j.issn.1001-9081.2019091646

Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (5): 1301-1308.DOI: 10.11772/j.issn.1001-9081.2019091646

• Artificial intelligence • Previous Articles Next Articles

Microscopic image identification for small-sample Chinese medicinal materials powder based on deep learning

WANG Yiding¹, HAO Chenyu¹, LI Yaoli², CAI Shaoqing², YUAN Yuan³

1.School of Information, North China University of Technology, Beijing 100144, China
2.School of Pharmaceutical Sciences, Peking University, Beijing 100191, China
3.National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China

Received:2019-09-26 Revised:2019-11-01 Online:2020-05-10 Published:2020-05-15
Contact: CAI Shaoqing, born in 1960, Ph. D., professor. His research interests include identification and quality evaluation of crude drugs.
About author:WANG Yiding, born in 1967, Ph. D., professor. His research interests include biometrics recognition, machine vision.HAO Chenyu, born in 1996, M. S. candidate. His research interests include computer vision, deep learning.LI Yaoli, born in 1977, Ph. D., lecturer. Her research interests include identification and quality control of traditional Chinese medicines.CAI Shaoqing, born in 1960, Ph. D., professor. His research interests include identification and quality evaluation of crude drugs.YUAN Yuan, born in 1978, Ph. D., research fellow. Her research interests include identification of traditional Chinese medicines, molecular pharmacognosy.
Supported by:
This work is partially supported by the Specific Fund of Traditional Chinese Medicine Industry（201407003），the Key Project of Increase and Decrease of Expenditure at the Central Government Level（2060302）.

基于深度学习的小样本中药材粉末显微图像识别

王一丁¹, 郝晨宇¹, 李耀利², 蔡少青², 袁媛³

1.北方工业大学信息学院，北京 100144
2.北京大学药学院，北京 100191
3.中国中医科学院中药资源中心，北京 100700

通讯作者: 蔡少青(1960—)
作者简介:王一丁(1967—)，男，北京人，教授，博士，CCF会员，主要研究方向：生物特征识别、机器视觉；郝晨宇(1996—)，男，北京人，硕士研究生，主要研究方向：计算机视觉、深度学习；李耀利(1977—)，女，陕西宝鸡人，讲师，博士，主要研究方向：中药鉴定和质量评价；蔡少青(1960—)，男，宁夏银川人，教授，博士，主要研究方向：生药品种鉴定和品质评价；袁媛（1978—），女，河北保定人，研究员，博士，主要研究方向：中药鉴定、分子生药学。
基金资助:
中医药行业科研专项(201407003)；中央本级重大增减支项目(2060302)。

Abstract

Abstract:

Aiming at the problems that a wide variety of Chinese medicinal materials have small samples, and it is difficult to classify the vessels of them, an improved convolutional neural network method was proposed based on multi-channel color space and attention mechanism model. Firstly, the multi-channel color space was used to merge the RGB color space with other color spaces into 6 channels as the network input, so that the network was able to learn the characteristic information such as brightness, hue and saturation to make up for the insufficient samples. Secondly, the attention mechanism model was added to the network, in which the two pooling layers were connected tightly by the channel attention model, and the multi-scale cavity convolutions were combined by the spatial attention model, so that the network focused on the key feature information in the small samples. Aiming at 8 774 vessel images of 34 samples collected from Chinese medicinal materials, the experimental results show that by using the multi-channel color space and attention mechanism model method, compared with the original ResNet network, the accuracy is increased by 1.8 percentage points and 3.1 percentage points respectively, and the combination of the two methods increases accuracy by 4.1 percentage points. It can be seen that the proposed method greatly improves the accuracy of small-sample classification.

Key words: small-sample data, attention mechanism, color space, Convolutional Neural Network (CNN), Chinese medicinal materials powder

摘要：

针对中药材种类繁多、数据量稀少以及难以对其导管进行分类的问题，提出一种基于多通道颜色空间与注意力机制模型的卷积神经网络改进方法。首先，采用多通道颜色空间将RGB颜色空间与其他颜色空间合并为6通道作为网络输入，使网络学习亮度、色调和饱和度等特征信息，弥补数据量的不足；其次，在网络中加入注意力机制模型，其中通道注意力模型将两个池化层紧密连接到一起，空间注意力模型将多尺度空洞卷积结合到一起，使网络将注意力聚焦于小样本中关键的特征信息。实验结果表明，针对34种中药材样本的8 774张导管图像，采用多通道颜色空间和注意力机制模型的方法，与原始ResNet网络相比，准确率分别提升了1.8个百分点和3.1个百分点，将二者结合后准确率提升了4.1个百分点，说明所提方法对小样本分类的准确率有着大幅度的提升。

关键词: 小样本数据, 注意力机制, 颜色空间, 卷积神经网络, 中药材粉末

CLC Number:

TP391.41

WANG Yiding, HAO Chenyu, LI Yaoli, CAI Shaoqing, YUAN Yuan. Microscopic image identification for small-sample Chinese medicinal materials powder based on deep learning[J]. Journal of Computer Applications, 2020, 40(5): 1301-1308.

王一丁, 郝晨宇, 李耀利, 蔡少青, 袁媛. 基于深度学习的小样本中药材粉末显微图像识别[J]. 计算机应用, 2020, 40(5): 1301-1308.

References

1 徐国均,徐珞珊,刘柏英,等 . 中药材粉末显微鉴定[M]. 北京:人民卫生出版社, 1986: 14-608. XU G J , XU L S , LIU B Y , et al . Microscopic identification of Powdered Chinese Medicinal Materials[M]. Beijing: People’s Medical Publishing House, 1986: 14-608.
2 中华人民共和国卫生部药典委员会 . 中华人民共和国药典[M]. 北京:人民卫生出版社, 1977: 4-308. (Chinese Pharmacopoeia Commission. Chinese Pharmacopoeia[M]. Beijing: People’s Medical Publishing House, 1977: 4-308.)
3 中华人民共和国卫生部药典委员会 . 中华人民共和国药典中药粉末显微鉴别彩色图集[M]. 广州:广东科技出版社, 1999: 3-374. (Chinese Pharmacopoeia Commission. Colored Collection of Microscopic Identification of Chinese Material Medica Powder from Chinese Pharmacopoeia [M]. Guangzhou: Guangdong Science and Technology Press, 1999: 3-374.)
4 赵中振 . 中药显微鉴别图鉴[M]. 沈阳:辽宁科学技术出版社, 2005: 1-323. (ZHAO Z Z. An Illustrated Microscopic Identification of Chinese Material Medica[M]. Shenyang: Liaoning Science and Technology Publishing House, 2005: 1-323.)
5 赵中振,陈虎彪 . 中药显微鉴定图典[M]. 福州:福建科学技术出版社, 2016: 17-335. (ZHAO Z Z, CHEN H B. Chinese Medicinal Microscopic Identification[M]. Fuzhou: Fujian Science and Technology Publishing House, 2016: 17-335.)
6 王一丁,石铎,李耀利,等 . 基于SqueezeNet深度网络的中药材粉末显微特征图像识别研究[J]. 电子显微学报, 2019, 38(2): 130-138. WANG Y D , SHI D , LI Y L , et al . Studies on identification of microscopic images of Chinese medicinal materials powder based on SqueezeNet deep network[J]. Journal of Chinese Electron Microscopy Society, 2019, 38(2): 130-138.
7 HINTON G E , SALAKHUTDINOV R R . Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786): 504-507.
8 LECUN Y , BOTTOU L , BENGIO Y , et al . Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324.
9 SIMONYAN K , ZISSERMAN A . Very deep convolutional networks for large-scale image recognition[EB/OL]. [2019-05-20].https://arxiv.org/pdf/1409.1556.pdf.
10 SZEGEDY C , LIU W , JIA Y , et al . Going deeper with convolutions[C]// Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2015: 1-9.
11 HE K , ZHANG X , REN S , et al . Deep residual learning for image recognition[C]// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2016: 770-778.
12 HE K , ZHANG X , REN S , et al . Identity mappings in deep residual networks[C]// Proceedings of the 2016 European Conference on Computer Vision, LNCS 9908. Cham: Springer, 2016: 630-645.
13 韩宏,王鹏,唐振民,等 . 彩色图像中复杂背景的多人脸检测[J]. 南京理工大学学报(自然科学版), 2001, 25(6): 597-601. HAN H , WANG P , TANG Z M , et al . Multiple human face detection in color image with complex background[J]. Journal of Nanjing University of Science and Technology (Natural Science Edition), 2001, 25(6): 597-601.
14 杨红玲,宣士斌,莫愿斌,等 . 基于YCbCr颜色空间手势分割[J]. 广西民族大学学报(自然科学版), 2017, 23(3): 61-66. YANG H L , XUAN S B , MO Y B , et al . Based on the gesture segmentation YCbCr color space[J]. Journal of Guangxi University for Nationalities (Natural Science Edition), 2017, 23(3): 61-66.
15 陈昌红,刘彬,张浩 . 基于多通道和卷积神经网络的极光分类[J]. 计算机技术与发展, 2018, 28(12): 200-204. (CHNE C H, LIU B, ZHANG H. Aurora images classification based on multi-channel fusion and convolutional neural network[J]. Computer Technology and Development, 2018, 28(12): 200-204.)
16 江凤兵 . 基于RGB-H-CbCr新颜色空间的肤色检测算法研究[J]. 科技广场, 2011(9): 34-38. (JIANG F B. The study of skin detection algorithms based on the RGB-H-CbCr new color space[J]. Science Mosaic, 2011(9): 34-38.)
17 CHOLLET F . Xception: deep Learning with depthwise separable convolutions[C]// Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2017: 1800-1807.
18 XIE S , GIRSHICK R , DOLLáR P , et al . Aggregated residual transformations for deep neural networks[C]// Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2017:5987-5995.
19 HU J , SHEN L , SUN G . Squeeze-and-excitation networks[C]// Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2018: 7132-7141.
20 WOO S, PARK J , LEE J Y, et al . CBAM: convolutional block attention module[C]// Proceedings of the 2018 European Conference on Computer Vision, LNCS 11211. Cham: Springer, 2018: 3-19.
21 LI X , WANG W HU X , et al . Selective kernel networks[EB/OL]. [2019-08-20].https://arxiv.org/pdf/1903.06586.pdf.

[1]	DAI Yurou, YANG Qing, ZHANG Fengli, ZHOU Fan. Trajectory prediction model of social network users based on self-supervised learning [J]. Journal of Computer Applications, 2021, 41(9): 2545-2551.
[2]	LIU Yaxuan, ZHONG Yong. Joint extraction method of entities and relations based on subject attention [J]. Journal of Computer Applications, 2021, 41(9): 2517-2522.
[3]	SONG Zhongshan, LIANG Jiarui, ZHENG Lu, LIU Zhenyu, TIE Jun. Remote sensing scene classification based on bidirectional gated scale feature fusion [J]. Journal of Computer Applications, 2021, 41(9): 2726-2735.
[4]	LI Kangkang, ZHANG Jing. Multi-layer encoding and decoding model for image captioning based on attention mechanism [J]. Journal of Computer Applications, 2021, 41(9): 2504-2509.
[5]	ZHANG Yongbin, CHANG Wenxin, SUN Lianshan, ZHANG Hang. Detection method of domains generated by dictionary-based domain generation algorithm [J]. Journal of Computer Applications, 2021, 41(9): 2609-2614.
[6]	ZHAO Hong, KONG Dongyi. Chinese description of image content based on fusion of image feature attention and adaptive attention [J]. Journal of Computer Applications, 2021, 41(9): 2496-2503.
[7]	XU Jianglang, LI Linyan, WAN Xinjun, HU Fuyuan. Indoor scene recognition method combined with object detection [J]. Journal of Computer Applications, 2021, 41(9): 2720-2725.
[8]	WANG Hebing, ZHANG Chunmei. Facial landmark detection based on ResNeXt with asymmetric convolution and squeeze excitation [J]. Journal of Computer Applications, 2021, 41(9): 2741-2747.
[9]	CAO Yuhong, XU Hai, LIU Sun'ao, WANG Zixiao, LI Hongliang. Review of deep learning-based medical image segmentation [J]. Journal of Computer Applications, 2021, 41(8): 2273-2287.
[10]	QIN Binbin, PENG Liangkang, LU Xiangming, QIAN Jiangbo. Research progress on driver distracted driving detection [J]. Journal of Computer Applications, 2021, 41(8): 2330-2337.
[11]	HUANG Chengcheng, DONG Xiaoxiao, LI Zhao. Deep pipeline 5×5 convolution method based on two-dimensional Winograd algorithm [J]. Journal of Computer Applications, 2021, 41(8): 2258-2264.
[12]	DANG Weichao, LI Tao, BAI Shangwang, GAO Gaimei, LIU Chunxia. Real-time remaining life prediction method of Web software system based on self-attention-long short-term memory network [J]. Journal of Computer Applications, 2021, 41(8): 2346-2351.
[13]	ZENG Xiangyin, ZHENG Bochuan, LIU Dan. Detection of left and right railway tracks based on deep convolutional neural network and clustering [J]. Journal of Computer Applications, 2021, 41(8): 2324-2329.
[14]	TAN Daoqiang, ZENG Cheng, QIAO Jinxia, ZHANG Jun. Shadow detection method based on hybrid attention model [J]. Journal of Computer Applications, 2021, 41(7): 2076-2081.
[15]	WU Guangli, LI Leiting, GUO Zhenzhou, WANG Chengxiang. Video summarization generation model based on improved bi-directional long short-term memory network [J]. Journal of Computer Applications, 2021, 41(7): 1908-1914.

Microscopic image identification for small-sample Chinese medicinal materials powder based on deep learning

基于深度学习的小样本中药材粉末显微图像识别

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics