## 3 Experiments
### Experimental Results
The Capsule Neural Network model has a higher sensitivity and specificity for normal tissue, malignant tumor, and benign tumor. Its accuracy is also slightly better for malignant tumors and benign tumors. The segmentation outcomes of using the five mentioned models for two samples are shown in Figures 4 and 5. The segmentation performance of the five methods when looking at the segmented breast varies. The results show that the Deep Supervision, Capsule Neural Network, and Proposed Model achieved higher accuracy and sensitivity when compared to the Deep Features and HCOW methods. However, looking at the segmented breast, it can be observed that the fuzzy boundaries of the tumor in some images affected the segmentation performance of the five methods. The fuzzy boundaries of the tumor may be caused by the overlapping of tissue, and this can lead to misclassification of the normal tissue and malignant tumor. Figure 4: The segmentation results for sample 1 that were obtained underwent comparison through various methods, and their outputs were evaluated against the ground truth. The ground truth segmentation was represented in green, while the segmented output was shown in yellow. **(a)** Deep Supervision [16], **(b)** Capsule Neural Network [17], **(c)** Deep Features [18], **(d)** HCOW [19], and **(e)** Our framework. Moreover, the vague tumor shapes in some images can affect the segmentation performance of the methods. The tumor shapes were not clearly defined in some images, making it difficult for the models to accurately identify the boundaries of the tumor. This resulted in low sensitivity and accuracy for some of the methods. Additionally, the variation in tumor size can also affect the segmentation performance of the methods. Some images had small tumors while others had large tumors, and this led to variations in sensitivity and accuracy. The methods performed well on images with smaller tumors but struggled with larger tumors, resulting in low sensitivity and accuracy. Furthermore, the illumination variation in some images also affected the segmentation performance of the methods. Some images had bright illumination, while others had low illumination, and this led to variations in sensitivity and accuracy. The methods performed well on images with bright illumination but struggled with images with low illumination, resulting in low sensitivity and accuracy. A limitation of Deep Supervision [16] could be the requirement of a large amount of labeled data for training. This could be challenging in the medical imaging field, where collecting and annotating data can be time-consuming and expensive. Another limitation could be the computational resources required to train and evaluate the model, as the proposed method involves multiple stages and deep learning models, which could be computationally expensive. Another drawback of the method could be the sensitivity to initial conditions, which is a common issue in level set-based methods. The authors proposed a deep supervision approach to address this issue, but the performance could still be affected by the initial contour initialization. Additionally, the method may not be robust to variations in imaging quality or artifacts, which can be common in medical imaging. One disadvantage of Capsule Neural Network [17] is that it does not provide a clear explanation of the capsule network architecture used in the proposed model. Although the authors provide some details on the architecture, they do not explain the significance or advantages of using capsule networks over other deep learning architectures. This lack of clarity makes it difficult to understand the novelty of the proposed model and the contribution of the capsule network component to the overall performance of the model. Figure 5: The segmentation results for sample 2 that were obtained underwent comparison through various methods, and their outputs were evaluated against the ground truth. The ground truth segmentation was represented in green, while the segmented output was shown in yellow.