## 3 Experimental Setup and Result Analysis
### Experimental Results  
#### 3.4.1 Ablation Experiments  
To further investigate the impact of the various modules in the improved MobileNetV3 algorithm on the performance metrics of the base algorithm, ablation experiments were conducted on four datasets with different magnifications under the same experimental conditions. The base algorithm used was MobileNetV3. Subsequently, experiments were performed on different models by sequentially incorporating the SK, CA, and the SCA modules, and the multi-stage information fusion strategy into the feature extraction part of the backbone network. These are shown in Tables 2-5, combining the experimental results from the four datasets with different magnifications, the improved SCA module demonstrated  
Figure 5: SCA mechanism. superior performance compared to the CA module. Additionally, the newly incorporated mechanisms enhanced the algorithm's performance to varying degrees without conflicting with each other. The improved model, when compared to the benchmark MobileNetV3, showed enhancements in all performance metrics, including accuracy. The SK attention module, introduced in the initial two stages of the backbone network's feature extraction part, effectively extracted multi-scale features in breast cancer histopathology images. The SCA module, added in the latter two stages, focused on key features and captured finer, more detailed semantic features, enabling the recognition of subtle changes in the images. The multi-stage feature information strategy enhanced the network's ability to recognize pathological features by refining the feature extraction process at various stages. The experimental results confirm that the improved MobileNetV3 algorithm is highly effective for breast cancer pathology image classification, with the added components proving to be both scientific and effective. The number of model parameters and the computational load (FLOPs) for the benchmark MobileNetV3 model and its various improved versions are presented in Table 6. The \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **ACC** & **Precision** & **Recall** & **F1** \\ \hline MobileNetV3 & 90.025 & 89.214 & 87.911 & 88.46 \\ MobileNetV3\_CA & 91.771 & 90.221 & 89.113 & 89.606 \\ MobileNetV3\_SCA & 92.519 & 92.483 & 90.086 & 91.151 \\ MobileNetV3\_SK & 92.269 & 93.945 & 89.99 & 91.673 \\ MobileNetV3\_Strategy & 93.017 & 93.403 & 90.764 & 91.821 \\ MobileNetV3\_All & 95.511 & 94.909 & 95.34 & 95.028 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The ablation results of the dataset at 200\(\times\) magnification factor (%). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **ACC** & **Precision** & **Recall** & **F1** \\ \hline MobileNetV3 & 91.272 & 89.485 & 93.397 & 91.168 \\ MobileNetV3\_CA & 93.766 & 93.897 & 92.202 & 92.941 \\ MobileNetV3\_SCA & 95.012 & 94.18 & 95.602 & 94.65 \\ MobileNetV3\_SK & 92.519 & 93.476 & 89.562 & 91.337 \\ MobileNetV3\_Strategy & 94.015 & 94.187 & 92.603 & 93.329 \\ MobileNetV3\_All & 96.259 & 95.729 & 95.789 & 95.726 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The ablation results of the dataset at 40\(\times\) magnification factor (%). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **ACC** & **Precision** & **Recall** & **F1** \\ \hline MobileNetV3 & 91.272 & 89.485 & 93.397 & 91.168 \\ MobileNetV3\_CA & 93.766 & 93.897 & 92.202 & 92.941 \\ MobileNetV3\_SCA & 95.012 & 94.18 & 95.602 & 94.65 \\ MobileNetV3\_SK & 92.519 & 93.476 & 89.562 & 91.337 \\ MobileNetV3\_Strategy & 94.015 & 94.187 & 92.603 & 93.329 \\ MobileNetV3\_All & 96.259 & 95.729 & 95.789 & 95.726 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The ablation results of the dataset at 100\(\times\) magnification factor (%).