Computer Engineering and Applications ›› 2022, Vol. 58 ›› Issue (11): 200-208.DOI: 10.3778/j.issn.1002-8331.2106-0186

• Graphics and Image Processing • Previous Articles     Next Articles

Epidermal Cell Image Recognition Research of Improved EfficientNet

WANG Yiding, YAO Yi, LI Yaoli, CAI Shaoqing, YUAN Yuan   

  1. 1.School of Information Science and Technology, 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
  • Online:2022-06-01 Published:2022-06-01



  1. 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

Abstract: The amount of microscopic image data of traditional Chinese medicinal materials powder is small, and there are certain differences in features and shapes of different production areas and different collection environments. The traditional image classification methods have poor recognition results under cross-database condition. To solve the above problems, it proposes an improved method of deep convolutional neural network based on multi-channel fusion and SPP structure. First, it combines feature maps obtained by Canny edge detection and local binary pattern with original image to form a five-channel image and then it is sent to the network, in order to expand the data width of the network input; second, it embeds the improved SPP module in the EfficientNet network, in order to increase the depth of the network. The above methods can make the network pay more attention to the deep texture information of image, so that it is not affected by the collection environment, etc. , and solves the problem of cross-database recognition. The experimental results show that for two different batches of microscopic images of the epidermal cells of Chinese medicinal materials of 26 kinds, using library 1 as the training set and library 2 as the test set, the accuracy rate has increased by 2.7 percentage points to 81.5%, which proves the proposed research method has certain advantages for the task of classification of microscopic images of traditional Chinese medicinal materials under cross-database condition.

Key words: convolutional neural network, image classification of microscopic features of Chinese medicinal materials powder, multi-channel fusion, cross-database, deep learning

摘要: 中药材粉末显微图像数据量少,而且在不同产地、不同采集环境下的特征形态具有一定的差异,传统的图像分类方法跨库识别效果不佳。针对以上问题提出一种基于多通道融合和SPP结构的深度卷积神经网络改进方法。采用Canny边缘检测和局部二值模式的特征图与原图合并形成五通道的图像送入网络,扩充网络输入端的数据宽度;将改进的SPP模块嵌入到EfficientNet网络中,增加网络的深度。以上方法可以使网络更加注重图像的深层纹理信息,从而不受采集环境等的影响,很好地解决跨库识别问题。实验结果表明,对26种中药材粉末表皮细胞显微图像的两个不同批次数据,采用1库作为训练集,2库作为测试集,其准确率提升了2.7个百分点,达到81.5%,证明了所提研究方法对于跨库中药材显微图像分类任务具有一定的优势。

关键词: 卷积神经网络, 中药材粉末显微特征图像识别, 多通道融合, 跨数据库, 深度学习