北京生物医学工程

基于深度学习的中药材饮片图像识别

Identification of the images of Chinese herb slices with deep learning

作者：刘加峰高子啸段元民李海云石宏理

单位：首都医科大学生物医学工程学院（北京100069）通信作者:石宏理，副教授。E-mail: shl@ccmu.edu.cn <p> </p>

分类号：R318. 04 <p> </p>

出版年·卷·期（页码）：2021·40·6（605-608）

摘要：

目的建立一个基于深度卷积神经网络的中药饮片图像检测识别系统。该系统对于正常情况下采集的中药饮片图像，能够自动检测识别出相应类别的中药饮片。方法本文使用了 SSD目标检测算法，构建数据集，利用标注工具进行了标注，然后在云端colab上进行调试代码、训练、测试、验证。结果对于3种中药饮片（枸杞、甘草、陈皮）进行识别验证，平均识别率高于80%,样本集足够大可以有效提高识别准确率。结论本文将卷积神经网络应用于中药材识别中，将传统的中医学与新兴的深度学习网络相结合,识别中药饮片的效率高,速度快，准确率高，可应用于绝大部分需要识别中药饮片类别的场景。

Objective A deep learning-convolutional neural network based image detection and recognition system for Chinese herbal slices is built. The system is capable of automatically detecting and recognizing categories and locating the images of traditional Chinese medicine drinking tablets that contain multiple categories established under simulated normal conditions. Methods In this paper, we used the SSD target detection algorithm ? established the image database, labeled them using the labeling tool, and then debugged the code, trained, tested, and verified them on the cloud colab. Results For the three Chinese herbal slices ( Chinese wolfberry, licorice, and pericarpium citri reticulatae) , the average recognition rate was more than 80%, and in particular, if the sample set was large enough then the recognition accuracy was improved. Conclusions In this paper, convolution neural network is applied to the identification of traditional Chinese herbs, which combines traditional Chinese medicine with the new deep learning network. It has high efficiency, fast speed and high accuracy. It can be applied to most scenes that need to identify the categories of traditional Chinese herbs.

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