Image recognition of traditional Chinese medicine based on deep learning

被引：25

作者：

Miao, Junfeng ^{[1
]}

Huang, Yanan ^{[2
]}

Wang, Zhaoshun ^{[1
]}

Wu, Zeqing ^{[3
]}

Lv, Jianhui ^{[4
]}

机构：

[1] Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Beijing, Peoples R China

[2] Ezhou Vocat Univ, Business Sch, Ezhou, Hubei, Peoples R China

[3] Xinxiang Med Univ, Sch Pharm, Xinxiang, Peoples R China

[4] Pengcheng Lab, Shenzhen, Peoples R China

来源：

FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY | 2023年 / 11卷

关键词：

traditional Chinese medicinal; deep learning; classification; image recognition; network models; CLASSIFICATION; IDENTIFICATION;

D O I：

10.3389/fbioe.2023.1199803

中图分类号：

Q81 [生物工程学（生物技术）]; Q93 [微生物学];

学科分类号：

071005 ; 0836 ; 090102 ; 100705 ;

摘要：

Chinese herbal medicine is an essential part of traditional Chinese medicine and herbalism, and has important significance in the treatment combined with modern medicine. The correct use of Chinese herbal medicine, including identification and classification, is crucial to the life safety of patients. Recently, deep learning has achieved advanced performance in image classification, and researchers have applied this technology to carry out classification work on traditional Chinese medicine and its products. Therefore, this paper uses the improved ConvNeXt network to extract features and classify traditional Chinese medicine. Its structure is to fuse ConvNeXt with ACMix network to improve the performance of ConvNeXt feature extraction. Through using data processing and data augmentation techniques, the sample size is indirectly expanded, the generalization ability is enhanced, and the feature extraction ability is improved. A traditional Chinese medicine classification model is established, and the good recognition results are achieved. Finally, the effectiveness of traditional Chinese medicine identification is verified through the established classification model, and different depth of network models are compared to improve the efficiency and accuracy of the model.

引用

页数：10

共 46 条

[1] Identification of haploid and diploid maize seeds using convolutional neural networks and a transfer learning approach [J].

Altuntas, Yahya ;

Comert, Zafer ;

Kocamaz, Adnan Fatih .

COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2019, 163

[2]

Artzai P., 2019, COMPUT ELECTRON AGR, V2019

[3] Body-movement-based human identification using convolutional neural network [J].

Batchuluun, Ganbayar ;

Naqvi, Rizwan Ali ;

Kim, Wan ;

Park, Kang Ryoung .

EXPERT SYSTEMS WITH APPLICATIONS, 2018, 101 :56-77

[4] Machine Learning Approaches in Traditional Chinese Medicine: A Systematic Review [J].

Chen, Haiyang ;

He, Yu .

AMERICAN JOURNAL OF CHINESE MEDICINE, 2022, 50 (01) :91-131

[5]

Chen WT, 2021, INFORM MED UNLOCKED, V25, P100607, DOI [10.1016/j.imu.2021.100607, 10.1016/j.imu.2021.100607, DOI 10.1016/J.IMU.2021.100607]

[6] Research on identification method of tangerine peel year based on deep learning [J].

Chu, Ziyi ;

Li, Fengmei ;

Wang, Dongwei ;

Xu, Shusheng ;

Gao, Chunfeng ;

Bai, Haoran .

FOOD SCIENCE AND TECHNOLOGY, 2022, 42

[7]

Congying Xing, 2020, 2020 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA), P140, DOI 10.1109/AIEA51086.2020.00037

[8]

Dosovitskiy A., 2021, P 9 INT C LEARN REPR, P2021

[9] A Convolutional Neural Network Classifier Identifies Tree Species in Mixed-Conifer Forest from Hyperspectral Imagery [J].

Fricker, Geoffrey A. ;

Ventura, Jonathan D. ;

Wolf, Jeffrey A. ;

North, Malcolm P. ;

Davis, Frank W. ;

Franklin, Janet .

REMOTE SENSING, 2019, 11 (19)

[10]

Fukushima K., 1982, COMPETITION COOPERAT, P267

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