基于深度学习的小样本中药材粉末显微图像识别

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

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8774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。

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 multichannel 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 8774 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.