# A Multi-Scale Feature Extraction Algorithm for Chinese Herbal Medicine Image Classification
## 6 Conclusions  
This study constructed a highly complex Chinese herbal medicine image dataset, which significantly surpasses existing datasets in terms of morphological diversity and inter-class similarity, more closely resembling the complexity of real-world scenarios. In response to the characteristics of Chinese herbal medicine images, particularly their inter-class similarity and stacked arrangement, this study innovatively proposes a multi-scale representation module and designs a feature extraction unit, FACNBlock, leading to the construction of the MSPyraNet. The experimental results demonstrate that the MSPyraNet exhibits exceptional performance in the task of Chinese herbal medicine image classification, particularly in handling complex scenarios and distinguishing between morphologically similar Chinese herbal medicine categories, showcasing significant advantages. Although the current dataset already exhibits complex and diverse characteristics, the number of  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**True Label** & **Method** & **ConvNeXtv2\_b** & **CSA** & **MSPyraNet (Ours)** \\ \hline \multirow{2}{*}{Flos Sophorae} & Flos Sophorae & 72.37\% & 59.21\% & **94.74\%** \\  & Honeyusuckle & 23.68\% & 38.16\% & **0.00\%** \\ \hline \multirow{2}{*}{Honeysuckle} & Honeyusuckle & 88.89\% & 76.39\% & **98.61\%** \\  & Folium Artemisiae Argyi & 6.94\% & 8.33\% & **0.00\%** \\  & Flos Sophorae & 4.17\% & 12.50\% & **1.39\%** \\ \hline \multirow{3}{*}{Dangshen} & Dangshen & 20.83\% & 30.56\% & **75.00\%** \\  & Angelica Sinensis & 12.50\% & 22.22\% & **4.17\%** \\  & Flos Sophorae & 22.22\% & 13.89\% & **0.00\%** \\  & Rhizoma Phragmitis & 26.39\% & 16.67\% & **5.56\%** \\ \hline \multirow{2}{*}{White Hyacinth Bean} & White Hyacinth Bean & 59.62\% & 61.54\% & **76.92\%** \\  & Poria Cocos & 23.08\% & 26.92\% & **15.38\%** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of classification performance for Chinese herbal medicine with high inter-class similarity.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline Split Method 1 & 89.22\% & 88.85\% & 87.87\% & 88.16\% \\ Split Method 2 & 88.96\% & 88.81\% & 88.43\% & 88.69\% \\ Split Method 3 & 89.82\% & 89.68\% & 89.52\% & 89.61\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Experiments under different dataset splits.  
samples is still relatively limited. In the future, we plan to further expand the dataset to provide a richer and more reliable resource for research in this field.  
Conceptualization, J.M. and W.D.; methodology, W.D.; software, W.D. and Y.M.; validation, W.D., Y.M. and Y.F.; formal analysis, W.D. and Y.M.; investigation, Y.F. and W.D.; resources, J.M.; data curation, W.D. and Y.F.; writing--original draft preparation, W.D. and Y.M.; writing--review and editing, W.D. and J.M.; visualization, W.D.; supervision, J.M.; project administration, J.M.; funding acquisition, J.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Gansu Province, grant number No. 23 JRRA1066; the Central Government Guided Local Science and Technology Development Fund Project, grant number No. 25 ZYJ016; the Gansu Province Major Special Science and Technology Project, grant number No. 22 ZD6 GE016; and the Fundamental Research Funds for the Central Universities, grant number No. lzujbky-2024-eyt02.  
Not applicable.  
Due to intellectual property considerations, the dataset and code required to reproduce the findings of this study can be provided by contacting the corresponding author upon request.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
CHMD1 Chinese Herbal Medicine Dataset 1 CHMD2 Chinese Herbal Medicine Dataset 2