Abstract:

A multi-scale 2D-Adaboost algorithm was proposed to solve the problem that the microscopic images of Chinese medicinal materials powder contain a large number of fine features and background interference factors， which leads to excessive changes in the same medicinal materials （large differences within the class） and too similar features among various medicinal materials （small differences between the classes）. Firstly， a global-local feature fusion backbone network architecture was constructed to extract multi-scale features better. By combining the advantages of Transformer and Convolutional Neural Network （CNN）， this architecture was able to extract and fuse global and local features at various scales effectively， thereby improving the feature capture capability of the backbone network significantly. Secondly， the single-scale output of Adaboost was extended to multi-scale output， and a 2D-Adaboost structure-based background suppression module was constructed. With this module， the output feature maps of each scale of the backbone network were divided into foreground and background， thereby suppressing feature values of the background region effectively and enhancing the strength of discriminative features. Finally， an extra classifier was added to each scale of the 2D-Adaboost structure to build a feature refinement module， which coordinated the collaborative learning among the classifiers by controlling temperature parameters， thereby refining the feature maps of different scales gradually， helping the network to learn more appropriate feature scales， and enriching the detailed feature representation. Experimental results show that the recognition accuracy of the proposed algorithm reaches 96.85%， which is increased by 7.56， 5.26， 3.79 and 2.60 percentage points， respectively， compared with those of ConvNeXt-L， ViT-L， Swin-L， and Conformer-L models. The high accuracy and stability of the classification validate the effectiveness of the proposed algorithm in classification tasks of Chinese medicinal materials powder microscopic images.

Key words: deep learning, Chinese medicinal material, microscopic image recognition, feature fusion, 2D-Adaboost

摘要：

针对中药材粉末的显微图像中含有大量细微特征和背景干扰因素导致的同一类药材的变化过大（类内差异大）和多种药材之间特征过于相似（类间差异小）的问题，提出一种多尺度2D-Adaboost算法。首先，构建一个全局?局部特征融合的主干网络架构，以更好地提取多尺度特征，该架构通过结合Transformer和卷积神经网络（CNN）的优势能有效提取并融合各个尺度的全局和局部特征，从而显著提高主干网络的特征捕捉能力；其次，将Adaboost的单尺度输出拓展到多尺度，并构建2D-Adaboost结构的背景抑制模块，该模块将主干网络各个尺度的输出特征图划分为前景和背景，从而有效抑制背景区域的特征值，并增加判别性特征的强度；最后，在2D-Adaboost结构的每个尺度上额外添加一个分类器以构建特征细化模块，该模块通过控制温度参数协调分类器间的协作学习，从而逐步细化不同尺度的特征图，帮助网络学习更合适的特征尺度，并丰富细节特征的表示。实验结果表明，所提算法的识别准确率达到了96.85%，与ConvNeXt-L、ViT-L、Swin-L和Conformer-L模型相比分别上升了7.56、5.26、3.79和2.60个百分点。高准确率和分类效果的稳定性验证了所提算法在中药材粉末显微图像分类任务中的有效性。

关键词: 深度学习, 中药材, 显微图像识别, 特征融合, 2D-Adaboost