## 3 Experimental Setup and Result Analysis
### Experimental Results
#### 3.4.4 Confusion Matrix  
The confusion matrix is a widely used evaluation metric in classification problems, illustrating the relationship between actual and predicted categories. In the context of breast cancer histopathology image classification, where the dataset includes various pathology types, the confusion matrix helps visualize how each category is accurately or inaccurately classified. Therefore, this section presents the confusion matrices corresponding to the test results of the model with different modules, using the dataset at 40\(\times\) magnification as an example, as shown in Figure 7. The pathological subtypes are shown as follows: adenosis (A), fibroadenoma (F), tubular adenoma (TA), phyllodes tumor (PT), papillary carcinoma (PC), ductal carcinoma (DC), lobular carcinoma (LC), mucinous carcinoma (MC). \begin{table}
\begin{tabular}{c c c c c c} \hline
**Magnification Factor** & **Model** & **ACC** & **Precision** & **Recall** & **F1** \\ \hline \multirow{4}{*}{200\(\times\)} & MobileNetV3 & 90.025 & 89.214 & 87.911 & 88.46 \\  & ResNet18 & 84.987 & 86.574 & 82.385 & 84.783 \\  & EfficientNetB0 & 88.95 & 88.652 & 87.185 & 87.264 \\  & MobileNetV3\_All & 95.511 & 94.909 & 95.34 & 95.028 \\ \hline \multirow{4}{*}{400\(\times\)} & MobileNetV3 & 87.282 & 85.26 & 86.006 & 85.041 \\  & ResNet18 & 80.263 & 82.382 & 78.329 & 79.867 \\  & EfficientNetB0 & 85.012 & 84.322 & 82.982 & 82.389 \\  & MobileNetV3\_All & 94.015 & 92.338 & 93.537 & 92.777 \\ \hline \end{tabular}
\end{table} 
Table 7: _Cont._The confusion matrix for the benchmark model MobileNetV3, shown in Figure 7a, reveals that ductal carcinoma was often misclassified as lobular carcinoma. Adenopathies, on the other hand, were consistently identified correctly, likely because their cell morphology is more regular, and their textures are clearer. Other pathology types, however, were subject to various degrees of misclassification. When the coordinate attention module was introduced in the last two stages of the feature extraction part of MobileNetV3, denoted as MobileNetV3_CA, there was a notable reduction in ductal carcinoma samples misclassified as lobular carcinoma, as shown in Figure 7b. This indicates an enhanced ability of the model to correctly identify ductal carcinoma with the addition of the CA module. Further improvement was seen with MobileNetV3_SCA, which integrates the improved SCA module into the last two stages of the feature extraction part of MobileNetV3. Figure 7c demonstrates that the number of misclassified samples was significantly reduced compared to both the benchmark model and MobileNetV3_CA. This suggests that the SCA module proposed in this paper is more effective than the CA module in classifying breast cancer histopathology image subtypes. MobileNetV3_SK, which incorporates the SK attention module into the first two stages of feature extraction of the benchmark model, shows an improved recognition ability for ductal carcinoma in Figure 7d. However, the model failed to correctly recognize all adenopathy cases. This could be because adding SK attention in the early stages of feature extraction enhanced the recognition of complex underlying texture information, which benefits the identification of samples with intricate pathological features like ductal carcinoma. However, for adenopathy images with simpler textures,  
Figure 6: ROC curve at various magnification factors. (**a**) ROC curve at 40\(\times\) magnification factor, (**b**) ROC curve at 100\(\times\) magnification factor, (**c**) ROC curve at 200\(\times\) magnification factor, (**d**) ROC curve at 400\(\times\) magnification factor. this increased focus on complex textures may introduce unnecessary interference, leading to the overanalysis of straightforward textures. Figure 7: Confusion matrix. (**a**) MobileNetV3, (**b**) MobileNetV3_CA, (**c**) MobileNetV3_SCA, (**d**) MobileNetV3_SK, (**e**) MobileNetV3_Stragegy, (**f**) MobileNetV3_All.