# A Multi-Scale Feature Extraction Algorithm for Chinese Herbal Medicine Image Classification
## 1 Introduction
Moreover, existing datasets often fail to adequately reflect the diversity of Chinese herbal medicine in real-world scenarios, leading to poor model performance in practical applications; (2) current research mainly involves simple improvements to CNNs or direct application of existing pre-trained models, which often struggle to effectively capture multi-scale features in Chinese herbal images, resulting in limited receptive fields and insufficient extraction of multi-level features. To address these issues, we have made contributions in two key areas. First, in terms of datasets, we expanded the CHMD1 dataset by adding four new types of Chinese herbal medicine and 933 sample images, constructing the CHMD2 dataset. This dataset retains the advantages of CHMD1 and significantly enhances inter-class similarity and morphological diversity, providing richer data support for model training. Second, in terms of model network improvement, we proposed an enhancement based on the ConvNeXtv2 architecture [19]. Specifically, we replaced the original 7 \(\times\) 7 large-kernel depthwise separable convolution [20] with a multi-scale representation module, creating the FACNBlock (Fusion Atrous Convolutional Network Block) unit tailored for Chinese herbal images capable of extracting multi-scale features. The FACNBlock can generate feature maps with diverse receptive fields, and it considers the characteristic that Chinese herbal medicines often appear in stacked forms. FACNBlock enables a more comprehensive and accurate capture of the multi-level features of Chinese herbal medicine images. Additionally, the FACNBlock incorporates Global Response Normalization (GRN) technology [19], significantly enhancing the expressive power between channels. GRN improves the contrast between feature map channels, alleviating feature collapse and enabling different channels to focus on extracting diverse feature information, thereby enhancing the model's expressive power. Based on this, we proposed the MSPraNet (Multi-Scale Pyramid Network), which fuses feature maps of different receptive fields to more comprehensively capture multi-level features of Chinese herbal images, further optimizing the performance of Chinese herbal image classification tasks. The comparison between the MSPraNet and other convolutional architecture can be seen in Figure 1. Experimental results show that our method performs well in Chinese herbal image classification tasks. The main contributions of this study include the following:  
1. Dataset Expansion and Improvement: We constructed the CHMD2 dataset, adding four new types of Chinese herbal medicine. This significantly enhanced inter-class similarity and morphological diversity, providing richer data support for model training;
2. Network Architecture Innovation: Proposed the MSPraNet, significantly improving the model's classification ability for Chinese herbal images through multi-scale feature extraction tailored to the characteristics of Chinese herbal images;
3. Experimental Results and Analysis: To validate the effectiveness of the proposed method, we conducted comparative experiments on the CHMD1 and CHMD2 datasets. The results show that the MSPraNet significantly outperforms baseline models.