## 3 Experiments
### Experimental Results  
Table 1 presents a comparison of the sensitivity, specificity, and accuracy results obtained from five distinct methods applied to the Mini-MIAS dataset for breast tumor segmentation. The five techniques are Deep Supervision [16], Capsule Neural Network [17], Deep Features [18], HCOW [19], and the proposed model. We trained each of the five methods on the Mini-MIAS dataset and evaluated their performance in terms of sensitivity, specificity, and accuracy. For each method, we calculated the performance metrics for normal tissue, malignant tumor, and benign tumor. Among the other models, Capsule Neural Network and HCOW achieved similar performance with sensitivity, accuracy, and specificity values ranging from 0.86 to 0.91 for malignant tumors and benign tumors. Deep Supervision and Deep Features also showed good performance, although they were slightly lower compared to Capsule Neural Network and HCOW. Deep Supervision achieved sensitivity, accuracy, and specificity values ranging from 0.85 to 0.90 for malignant and benign tumors, while Deep Features achieved sensitivity, accuracy, and specificity values ranging from 0.83 to 0.89 for malignant and benign tumors. We also analyzed the performance of each model for detecting false positives. The proposed model and Deep Supervision achieved the highest specificity for benign tumors, indicating that these models are less likely to produce false positives. On the other hand, HCOW and Deep Features showed slightly lower specificity values for benign tumors, indicating that these models may produce more false positives. Our results show that deep learning models can be highly effective in breast cancer diagnosis and tumor segmentation. The proposed model achieved the highest sensitivity and specificity values for all three tissue types, indicating that this model has the potential to accurately detect breast cancer and differentiate between normal tissue, malignant tumors, \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Structures**} & \multicolumn{3}{c}{**Sensitivity**} & \multicolumn{3}{c}{**Accuracy**} & \multicolumn{3}{c}{**Specificity**} \\ \cline{2-10}  & **Normal** & **Malignant** & **Benign** & **Normal** & **Malignant** & **Benign** & **Normal** & **Malignant** & **Benign** \\ \hline Deep Supervision [16] & 0.90 & 0.85 & 0.87 & 0.89 & 0.83 & 0.88 & 0.91 & 0.87 & 0.90 \\ Capsule Neural Network [17] & 0.91 & 0.87 & 0.89 & 0.91 & 0.88 & 0.89 & 0.90 & 0.86 & 0.88 \\ Deep Features [18] & 0.87 & 0.83 & 0.85 & 0.89 & 0.84 & 0.86 & 0.88 & 0.85 & 0.86 \\ HCOW [19] & 0.89 & 0.84 & 0.87 & 0.88 & 0.86 & 0.88 & 0.89 & 0.86 & 0.87 \\ Our model & **0.93** & **0.90** & **0.91** & **0.94** & **0.89** & **0.90** & **0.94** & **0.91** & **0.94** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of accuracy, specificity, and sensitivity outcomes using the Mini-MIAS Dataset for Breast Tumor Segmentation Models. The highest values are highlighted in bold. and benign tumors. The high performance of the proposed model may be attributed to the use of advanced deep learning techniques and the integration of multiple modalities. Capsule Neural Network and Deep Supervision also showed good performance for breast cancer diagnosis and tumor segmentation, although they were slightly lower compared to the proposed model. These models may be suitable for applications where high accuracy is not critical, or for situations where limited computational resources are available. HCOW and Deep Features also showed good performance, but they were slightly lower compared to Capsule Neural Network and HCOW. These models may be suitable for applications where moderate accuracy is acceptable, or for situations where the computational resources are limited. Comparing the two models, we can see that the Capsule Neural Network model performs slightly better than the HCOW model in terms of sensitivity, accuracy, and specificity. However, the differences between the values are not significant.