# A Multi-Scale Feature Extraction Algorithm for Chinese Herbal Medicine Image Classification
## 5 Experiments and Results Analysis
### Single Chinese Herb Prediction Accuracy Analysis  
To showcase MSPyraNet's capability in identifying individual Chinese herbal medicines, we analyzed the model's accuracy for each Chinese herb in the CHMD2 dataset. For a more intuitive comparison, the accuracy of CSA [9] and ConvNeXtv2_b [19] for each herb in CHMD2 was also presented. The results are shown in Table 7. The confusion matrices for accuracy of ConvNeXtv2_b [19], CSA [9], and MSPyraNet on the CHMD2 dataset can be found in Figures A1-A3 in Appendix A, respectively.  
The experimental results indicate that MSPyraNet's accuracy in identifying individual Chinese herbal medicines is significantly superior to that of CSA and ConvNeXtv2_b. Particularly notable improvements were observed for ganoderma lucidum, folium artemisiae argyi, dangshen, poria cocos, and flos sophorae.  
To further demonstrate MSPyraNet's enhanced performance in classifying Chinese herbal medicines with high inter-class similarity, a comparison was conducted of the probabilities of a given Chinese herb being predicted as the true label versus other morphologically similar labels. The results are presented in Table 8.  
The experimental results demonstrate that MSPyraNet exhibits superior performance in distinguishing between morphologically similar Chinese herbal medicines. For instance, flos sophorae, which is morphologically similar to honeysuckle, was frequently misclassified as honeysuckle by ConvNeXtv2_b and CSA. However, MSPyraNet did not misclassify flos sophorae as honeysuckle. Similarly, honeysuckle was often misclassified as folium artemisiae argyi and flos sophorae by ConvNeXtv2_b and CSA, but MSPyraNet did not misclassify honeysuckle as folium artemisiae argyi and only misclassified 1.39% of honeysuckle as flos sophorae. The identification accuracy of dangshen was notably low in ConvNeXtv2_b and CSA, with CSA achieving only 30.56%. However, MSPyraNet achieved an accuracy of 75.00% for dangshen, significantly reducing misclassifications such as an  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name of Chinese Herbal Medicine** & **ConvNeXtv2\_b** & **CSA** & **MSPyraNet (Ours)** \\ \hline Amomum Tsao-ko & 84.46\% & 92.57\% & **95.95\%** \\ Cordyceps Sinensis & 89.52\% & 86.29\% & **92.74\%** \\ Ganoderma Lucidum & 70.00\% & 76.00\% & **93.00\%** \\ Folium Artemisiae Argyi & 75.00\% & 63.16\% & **89.47\%** \\ White Hyacinth Bean & 59.62\% & 61.54\% & **76.92\%** \\ Radix Stemonae & 55.36\% & 60.71\% & **64.29\%** \\ Alumen & 92.31\% & 88.46\% & **100.00\%** \\ Lily & 79.00\% & 85.00\% & **92.00\%** \\ Radix Pacoiae Alba & 57.61\% & 53.26\% & **72.83\%** \\ Rhiza Atractylodis Macrocephalae & 61.36\% & 59.09\% & **70.45\%** \\ Mint & 100.00\% & 98.53\% & **99.26\%** \\ Angelica Sinensis & 73.08\% & 76.92\% & **82.69\%** \\ Dangshen & 20.83\% & 30.56\% & **75.00\%** \\ Poria Cocos & 75.00\% & 66.67\% & **92.86\%** \\ Raspberry & 93.27\% & 95.19\% & **93.27\%** \\ Matrimony Vine & 92.05\% & 97.73\% & **90.91\%** \\ Cinnamon & 82.00\% & 89.00\% & **95.00\%** \\ Flos Sophorae & 72.37\% & 59.21\% & **94.74\%** \\ Honeysuckle & 88.89\% & 76.39\% & **98.61\%** \\ Rhiza Phragmitis & 65.62\% & 68.75\% & **87.50\%** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Single Chinese herb identification accuracy on CHMD2.  
gelica diensis, flos sophorae, and rhizoma rhragmitis. The misclassification rate of white hyacinth bean as poria cocos was also reduced to 15.38%.