# A Multi-Scale Feature Extraction Algorithm for Chinese Herbal Medicine Image Classification
## 5 Experiments and Results Analysis
### Comparative Experiments with Common Models on CHMD1  
To validate the effectiveness of MSPyraNet in the task of Chinese herbal medicine classification, we conducted comparative experiments on the CHMD1 dataset. The performance of MSPyraNet was evaluated against several common deep learning models [17; 21; 22; 28; 29; 30] and CSA [9] in terms of accuracy, precision, recall, and F1 score. The classification layer of all models utilized a fully connected layer. Previous studies have shown that the CSA encoder outperforms other deep learning classification algorithms on the CHMD1 dataset, albeit with limited improvement. However, MSPyraNet demonstrated significantly superior performance on the CHMD1 dataset compared to other models. The specific experimental results are presented in Table 2.  
Experimental results demonstrate that MSPyraNet achieves superior performance, with an accuracy of 86.22%, precision of 83.80%, recall of 83.81%, and an F1 score of 83.26%. Compared to the CSA encoder, MSPyraNet shows improvements of 4.72%, 3.92%, 4.13%, and 3.83% in accuracy, precision, recall, and F1 score, respectively. Among all the SOTA models, EfficientNetv2_s performs the best, with scores of 79.94% (accuracy), 78.07% (precision), 77.99% (recall), and 77.28% (F1 score). MSPyraNet outperforms EfficientNetv2_s by 6.28% (accuracy), 5.73% (precision), 5.82% (recall), and 5.98% (F1 score). Within the Resnet family, MSPyraNet surpasses the best-performing model, Resnet-152, by 13.49% (accuracy), 9.42% (precision), 14.36% (recall), and 12.39% (F1 score). Similarly, compared to the best model in the ConvNeXt family, ConvNeXtv2_b, MSPyraNet achieves improvements of 7.85% (accuracy), 6.63% (precision), 7.86% (recall), and 7.56% (F1 score). These results demonstrate that MSPyraNet excels in the task of Chinese herbal medicine classification, marking a significant advancement over previous research. Figure 6 illustrates the performance comparison of MSPyraNet and SOTA models on the CHMD1 dataset across four evaluation metrics.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline ConvNeXtv2\_b & 78.37\% & 77.17\% & 75.95\% & 75.70\% \\ ConvNeXt\_b & 72.32\% & 70.21\% & 69.03\% & 69.34\% \\ ViT\_b\_16 & 76.80\% & 75.69\% & 73.33\% & 73.86\% \\ Swin\_Transformer & 74.92\% & 72.35\% & 71.72\% & 71.79\% \\ Resnet\_18 & 63.95\% & 60.70\% & 60.14\% & 60.14\% \\ Resnet\_50 & 69.59\% & 66.09\% & 66.98\% & 66.02\% \\ Resnet\_152 & 72.73\% & 74.38\% & 69.45\% & 70.87\% \\ EfficientNet\_b0 & 69.91\% & 68.52\% & 68.52\% & 66.88\% \\ EfficientNet\_b1 & 68.97\% & 66.54\% & 66.13\% & 66.08\% \\ EfficientNet\_b2 & 71.79\% & 69.82\% & 69.40\% & 68.89\% \\ EfficientNetv2\_s & 79.94\% & 78.07\% & 77.99\% & 77.28\% \\ EfficientNetv2\_m & 74.29\% & 73.21\% & 71.28\% & 71.38\% \\ CSA & 81.50\% & 79.88\% & 79.68\% & 79.43\% \\
**MSPyraNet (ours)** & **86.22\%** & **83.80\%** & **83.81\%** & **83.26\%** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparative experimental results of MSPyraNet on CHMD1.