## 3 Experiments
### Experimental Results
#### 3.5.2 Comparison with Other Methods
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**References**} & \multirow{2}{*}{**Methods**} & \multicolumn{4}{c}{**Image Level (\%)**} & \multicolumn{4}{c}{**Patient Level (\%)**} \\ \cline{3-10}  & & **40\(\times\)** & **100\(\times\)** & **200\(\times\)** & **400\(\times\)** & **40\(\times\)** & **100\(\times\)** & **200\(\times\)** & **400\(\times\)** \\ \hline [PERSON] et al. [43] & Multiple CNNs & 85.70 & 84.20 & 84.90 & 80.10 & 85.20 & 83.50 & 84.10 & 79.30 \\ [PERSON] et al. [44] & DE ensemble & 83.90 & 86.00 & 89.10 & 86.60 & 85.60 & 87.40 & 89.80 & 87.00 \\ [PERSON] et al. [22] & BreastNet & **97.99** & 97.84 & 98.51 & 95.88 & - & - & - & - \\ [PERSON] et al. [45] & VGG-VD16 & 95.03 & 90.41 & 88.48 & 85.00 & 95.50 & 91.57 & 89.20 & 89.20 \\ [PERSON] [46] & 22 layers CNN & 90.89 & 90.99 & 91.00 & 90.97 & 91.00 & 91.00 & 91.00 & 91.00 \\ [PERSON] et al. [16] & VGGNet16 & - & - & - & - & 94.11 & 95.12 & 97.01 & 93.40 \\ [PERSON] et al. [47] & Two-stage model & 96.70 & 97.60 & 95.00 & 93.30 & - & - & - & - \\ [PERSON] et al. [48] & Hcnn & 97.00 & 97.00 & 96.00 & 96.00 & - & - & - & - \\ [PERSON] et al. [49] & Hybrid architectures & 92.61 & 92.00 & 93.93 & 91.73 & - & - & - & - \\ [PERSON] et al. [50] & DRDA-Net & 95.72 & 94.41 & 97.43 & 96.84 & - & - & - \\ [PERSON] et al. [51] & ResNeXt+KD & 97.23 & 96.92 & 97.60 & 96.64 & - & - & - & - \\ [PERSON] et al. [52] & MPCS & 93.26 & 93.45 & 92.45 & 98.57 & 93.00 & 93.26 & 92.28 & 88.74 \\ [PERSON] et al. [53] & ResNeXt+adaptive & 94.37 & 93.85 & 91.63 & 93.31 & - & - & - & - \\ [PERSON] et al. [54] & Inception-V3 & 95.00 & 95.10 & 93.80 & 92.20 & 95.00 & 94.60 & 94.30 & 92.20 \\ [PERSON] et al. [55] & DenseNet 121+FCL & 96.19 & 96.53 & 94.02 & 94.50 & - & - & - & - \\ [PERSON] et al. [29] & ResNet101+SwinT & 97.50 & 96.60 & 96.30 & 96.10 & - & - & - & - \\ [PERSON] et al. [25] & ST-Double-Net & 97.47 & 96.86 & 97.25 & 95.05 & - & - & - & - \\ \hline \multirow{3}{*}{Ours} & SeONet & 95.49 & 95.84 & 98.01 & 95.05 & 94.50 & 96.02 & 97.89 & 95.82 \\  & RFNet & 95.82 & 94.56 & 98.34 & 94.50 & 94.79 & 95.27 & 98.44 & 94.67 \\ \cline{1-1}  & HoRFNet & 97.66 & **98.56** & **99.50** & **97.43** & **95.82** & **98.77** & **99.23** & **97.94** \\ \hline \hline \end{tabular}  
* “-” denotes that this method does not provide the experimental results.  
\end{table} 
Table 4: Comparison of classification accuracy at the image level and patient level using different methods. In addition, to comprehensively evaluate the performance of HoRFNet, we compare it with recent models based on the Transformer architecture. As shown in Table 4, at the image level, [PERSON] et al. [29] achieved accuracies of 97.50%, 96.60%, 96.30%, and 96.10% across different magnifications. ST-Double-Net [25] achieves corresponding accuracies of 97.47%, 96.86%, 97.25%, and 95.05%. HoRFNet consistently outperforms both Transformer-based methods at all magnification levels. In particular, at 100\(\times\) and 200\(\times\), HoRFNet improves upon ResNet101+SwinT by 1.96% and 3.20%, respectively, and surpasses ST-Double-Net by 1.70% and 2.25%, respectively. Therefore, although Transformer-based architectures have recently shown impressive performance in vision tasks, HoRFNet demonstrates superior discriminative power in the BCHI classification.