## 3 Experiments
### Ablation Study
#### 3.4.2 The Impact of the Structural Configuration of the RFB Module  
We conducted an ablation study on the BreakHis dataset at 100\(\times\) magnification to evaluate the effect of replacing different convolutional layers within HoRFNet with the receptive field block (RFB) module. The results are shown in Figure 6a. Specifically, we replaced the second, third, fourth, and fifth convolutional layers with RFB modules, cor  
Figure 5: The impact of Newton–Schulz iteration count on the classification accuracy of the model under 100\(\times\) magnification with the BreakHis dataset. Increasing the number of iterations enhances the model’s feature representation capabilities, but the effect tends to saturate after five iterations. At seven iterations or more, performance begins to degrade, accompanied by a significant rise in computational cost. responding to configurations numbered 1 through 4. The experimental results show that replacing the third convolutional layer yields the highest classification accuracy, with improvements of 2.40%, 1.60%, and 3.54% compared to replacing the second, fourth, and fifth layers, respectively. This suggests that the third convolutional layer plays a critical role in feature extraction, as it retains relatively high spatial resolution while also capturing semantic information. Integrating the multi-branch RFB module at this stage significantly enhances the model's feature representation capability. In contrast, replacing the fifth convolutional layer results in the poorest performance, likely because the RFB module is less effective at this deeper stage due to limited benefits from receptive field expansion. Based on this analysis, we select the third convolutional layer as the optimal insertion point for the RFB module in subsequent experiments. Building on this, we further investigate the impact of varying the number of branches within the RFB module on model performance, as shown in Figure 6a. We incrementally increase the number of branches: \"1\" represents using only the first branch, \"2\" includes the first and second branches, and so on, up to all four branches. The results show a consistent increase in classification accuracy as the number of branches increases, with the highest performance achieved when all four branches are included. Specifically, using four branches improves accuracy by 2.40%, 1.12%, and 0.80% compared to using only one, two, and three branches, respectively. This demonstrates that the multi-branch structure effectively simulates receptive fields at multiple scales, enhancing the model's ability to capture complex structures and diverse textures. The combination of different kernel sizes and dilation rates in each branch facilitates the extraction of key features across scales, improving the model's discriminative capacity. Therefore, we adopt the configuration with all four branches as the optimal RFB module setting in our model design. To further investigate the impact of dilation rate configuration on model performance, we conduct ablation experiments using five different sets of dilation rate combinations within the RFB module. The experimental results are shown in Figure 6b. The tested configurations include (1, 1, 1), (3, 3, 3), (5, 5, 5), (1, 3, 5), and (3, 3, 5), with each triplet corresponding to the dilation rates of the three branches in the RFB module. The results show that the (3, 3, 5) configuration achieves the highest classification accuracy among all settings, outperforming (1,1,1), (3,3,3), (5, 5, 5), and (1, 3, 5) by 0.80%, 0.48%, 1.04%, and 0.96%, respectively. These findings demonstrate that a progressively increasing dilation structure is more effective for modeling multi-scale receptive fields, thereby enhancing the model's ability to capture both global and local contextual information. In contrast, fixed dilation configurations such as (3, 3, 3) and (5, 5, 5), while capable of enlarging the receptive  
Figure 6: Ablation study results of RFB module.