摘要
为实现对常用60种中药饮片图像的精准快速识别,构建13 088张常见中药饮片图像数据集,采用迁移学习的方式,以深度学习算法中Xception卷积神经网络模型为基准,对饮片图像进行训练与识别。模型训练的初始学习率设置为0.01,优化器中设置Nesterov动量超参数为0.9,训练次数为100轮,得到在训练集上的分类准确率达到100%,验证集准确率为97.42%,测试集准确率为97.26%,最后结合混淆矩阵这一指标对模型的识别能力进行评估分析。该模型与传统依靠提取中药饮片图像特征的机器学习算法相比,分类效果更好,泛化能力更强。
In order to achieve accurate and fast recognition of images of 60 kinds of the traditional Chinese medicine pieces,a dataset of 13088 images of common traditional Chinese medicine pieces is built,and the transfer learning method is used to train and recognize the images of the traditional Chinese medicine pieces on the basis of the Xception convolutional neural network model in the depth learning algorithm.The initial learning rate for the model training is set as 0.01,the Nesterov momentum hyperparameter in the optimizer is set as 0.9,and the training times are set as 100.The results are obtained as follows.The accuracy of the training set is 100%,the accuracy of the verification set is 97.42%,and the accuracy of the test set is 97.26%.The recognition ability of the model is evaluated and analyzed by combining the confusion matrix.In comparison with the traditional machine learning algorithms that rely on extracting image features of the traditional Chinese medicine pieces,the proposed model has better classification effect and stronger generalization ability.
作者
张琦
区锦锋
周华英
ZHANG Qi;OU Jinfeng;ZHOU Huaying(College of Medical Information Engineering,Guangdong Pharmaceutical University,Guangzhou 510006,China;Guangdong Province Precise Medicine Big Data of Traditional Chinese Medicine Engineering Technology Research Center,Guangzhou 510006,China)
出处
《现代电子技术》
北大核心
2024年第3期29-33,共5页
Modern Electronics Technique
基金
广东省中医药局科研项目(20221221)
2023年广东省科技创新战略专项资金(“攀登计划”专项资金)(pdjh2023b0273)。
关键词
中药饮片
Xception
迁移学习
深度可分离卷积
混淆矩阵
分类效果
traditional Chinese medicine piece
Xception
transfer learning
depthwise separable convolution
confusion matrix
classification effect
