## 6 Discussion
### Performance Comparison with the SOTA Methods  
As shown in the related work section, few studies have addressed the multi-label classification of mammograms, with only [33] investigating this issue in recent years. The multi-label classification of mammogram images proposed by [PERSON] et al. [33] simultaneously classifies a mammogram into its abnormality type/findings (mass/mic-rocalcifications), severity level/pathology (benign/malignant), and density class (I-IV). They used ROIs as input for the deep learning module, transfer learning to initiate the VGG16-CNN weights with a fine-tuning technique, and a label powerset classifier for classification. The proposed algorithm considers the correlation between labels. They evaluated their method using the CBIS-DDSM [35], BCDR [40], INBreast [36], and MIAS [41] datasets with multiple metrics. On the other hand, the introduced system outperformed the SOTA methods as we used the fusion method and SOTA backbone model in addition to the multi-branch and channel attention techniques. It achieved a higher performance in all metrics on both the CBIS-DDSM and INBreast datasets. It is observed that fusing two view inputs improves the overall performance across all performance metrics when compared with a single view. This is because several irregularities and features might be visualized better in one view than in the other, and combining the features from two views leads to a comprehensive assessment of the breast situation. In addition, utilizing the SOTA ConvNeXt performs better than using other backbone models. Table 7 gives the performance metrics for the proposed and existing methods on the INBreast and CBIS-DDSM datasets. For the CBIS-DDSM dataset, [PERSON] et al. [33], who applied the VGG16 architecture, attained an F1-score of 0.935, a Hamming loss (HL) of 0.047, a mean average precision (mAP) of 0.895, a ranking loss (RL) of 0.087, a coverage (Cov) of 3.895, and an exact match (EM) of 0.822. The proposed method, which utilized the ConvNeXt architecture with a fusion technique, outperformed the existing method, achieving a higher F1-score of 0.947, a lower HL of 0.036, a higher mAP of 0.913, a lower RL of 0.07, a lower Cov of 3.66, and a higher EM of 0.869. For the INBreast dataset, [PERSON] et al. 's method [33] achieved the following performance metrics when testing their proposed method using the INBreast dataset: an F1-score of 0.935, a hamming loss of 0.047, a mean average precision of 0.895, a ranking loss of 0.087, a coverage of 3.895, and an exact match of 0.822. The proposed method outperformed their method, achieving a higher F1-score of 95.13, a lower HL of 0.032, a higher mAP of 0.928, a lower RL of 0.06565, a lower Cov of 3.557143, and a higher EM of 0.88857. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Reference** & **Method** & **F1** & **HL** & **mAP** & **RL** & **Cov** & **EM** \\ \hline \multicolumn{8}{c}{CBIB-DDSM} \\ \hline [PERSON] et al. [33] & VGG16 & 93.5\% \(\pm\) 0.019 & 0.047 \(\pm\) 0.022 & 89.5\% \(\pm\) 0.017 & 0.087 \(\pm\) 0.025 & 3.895 \(\pm\) 0.320 & 82.2\% \(\pm\) 0.041 \\ \hline Proposed method & ConvNeXt & 94.7\% \(\pm\) 0.01 & 0.036 \(\pm\) 0.007 & 91.3\% \(\pm\) 0.017 & 0.07 \(\pm\) 0.012 & 3.66 \(\pm\) 0.095 & 86.9\% \(\pm\) 0.019 \\ \hline \multicolumn{8}{c}{INBreast} \\ \hline [PERSON] et al. [33] & VGG16 & 94.2\% \(\pm\) 0.102 & 0.042 \(\pm\) 0.092 & 88.7\% \(\pm\) 0.140 & 0.082 \(\pm\) 0.125 & 3.723 \(\pm\) 0.147 & 82.7\% \(\pm\) 0.092 \\ \hline Proposed method & ConvNeXt & 95.1\% \(\pm\) 0.016 & 0.032 \(\pm\) 0.013 & 92.8\% \(\pm\) 0.025 & 0.065 \(\pm\) 0.021 & 3.55 \(\pm\) 0.174 & 88.9\% \(\pm\) 0.035 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The comparison of the proposed method with SOTA method on CBIS-DDSM and INBreast datasets. These results indicate the superiority of the proposed method compared with the existing methods for both the CBIS-DDSM and INBreast datasets. On the CBIS-DDSM dataset, our proposed method achieved improvement for all matrices.