## 3 Experiments
### Experimental Results
(**a**) Deep Supervision [16], (**b**) Capsule Neural Network [17], (**c**) Deep Features [18], (**d**) HCOW [19], and (**e**) Our framework. Another potential disadvantage of Capsule Neural Network [17] is that the authors do not provide a thorough analysis of the interpretability of the model. Although the proposed model achieves high accuracy in diagnosing breast cancer, it is unclear how the model arrives at its decision. This lack of interpretability can be problematic, as it may prevent clinicians from trusting and using the model in clinical practice. There are a few potential disadvantages of Deep Features [18]. Firstly, the paper uses a relatively small dataset (135 mammogram images), which could limit the generalizability of the results. A larger dataset could potentially lead to better performance and more reliable conclusions. Secondly, the paper uses a relatively simple deep learning model (AlexNet) for feature extraction, which may not be as effective as more advanced models. More recent models such as ResNet or EfficientNet may be able to extract more informative and discriminative features. Thirdly, the paper does not explicitly compare its results with other state-of-the-art methods for suspicious region segmentation in breast cancer mammogram images. This limits the ability to assess the relative performance of the proposed method compared to existing methods. Finally, the paper does not provide a detailed analysis of the impact of various hyper parameters on the performance of the proposed method. A more comprehensive hyper parameter tuning process could potentially lead to better performance. There are a few potential disadvantages of HCOW [19], including the following:  
* Limited evaluation: The paper only evaluates the HCOW method on a single dataset, the MIAS database, and does not compare its performance with other state-of-the-art methods. This limits the generalizability of the proposed method to other datasets and makes it difficult to compare its performance with other methods;
* Complexity: The HCOW method consists of multiple steps, including wavelet decomposition, histogram-based clustering, and adaptive multi-thresholding. This complexity may make it difficult for the method to be implemented in real-world clinical settings, where speed and ease of use are crucial;
* Lack of interpretability: The HCOW method does not provide any visual or quantitative explanation of how it makes its segmentation decisions. This lack of interpretability may limit its clinical utility, as clinicians may be hesitant to rely on a method that cannot explain its reasoning;
* Inadequate validation: The paper only reports sensitivity, specificity, and accuracy as performance metrics, which are inadequate for evaluating the performance of a segmentation method. Metrics such as Dice similarity coefficient and intersection over union are commonly used in the literature and provide a more comprehensive evaluation of the method's performance;
* Over-segmentation: The HCOW method has a tendency to over-segment regions, resulting in the inclusion of healthy tissue in the segmented regions. This may reduce the specificity of the method and increase the number of false positives. To determine whether the model exhibits bias towards different breast cancer types, a comprehensive evaluation would be necessary. This evaluation should involve analyzing the methodology, experimental setup, and results presented in Section 3 of the study. By examining this section, we can assess whether the model's performance is consistent across different breast cancer types or if there are any variations in its accuracy or effectiveness. To ensure that a model does not exhibit bias towards specific breast cancer types, it is important to carefully consider several factors during model development and evaluation:  
* Data representation: Ensure that the training dataset includes an adequate representation of different breast cancer types, including in situ and invasive breast cancers.