## 3 Experiments
### Evaluation Metrics  
When evaluating the performance of a deep learning model for breast tumor segmentation, specificity, recall, and accuracy are commonly used metrics in medical image analysis. Specificity measures the proportion of true negatives among all negative cases, indicating the model's ability to exclude healthy tissue and reduce the number of false positives. In contrast, recall measures the proportion of true positives among all positive cases, indicating the model's ability to accurately detect and segment tumors, reducing the number of false negatives. Accuracy measures the overall performance of the model by computing the proportion of correctly classified cases among all cases. However, accuracy can be misleading in imbalanced datasets, i.e., where one class has more cases than another [20; 60; 61; 62]. The combination of these metrics is typically used to evaluate the performance of a breast tumor segmentation model. High specificity and recall are desirable for accurate tumor segmentation, but the balance between these two metrics may depend on the specific application and clinical requirements. For example, in some cases, high recall may be more important to ensure that all tumors are detected, while, in other cases, high specificity may be more important to reduce unnecessary biopsies or surgeries [7; 13; 63]. However, these metrics are not the only factors to consider when evaluating a model's performance. Other factors, such as computational resources, interpretability, and ease of use in clinical settings, should also be taken into account. To evaluate the performance of our breast tumor segmentation model, we utilized these metrics (Equations (2)-(4)) to quantify the accuracy of the model and identify areas for improvement. Specificity, recall, and accuracy were calculated based on the number of true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN) in the test dataset. By analyzing these metrics, we gained insights into the model's effectiveness in detecting breast tumors and identified areas for improvement [7; 64; 65; 66; 67; 68]:  
\[\text{Specificity}=\text{TN}/(\text{TN}+\text{FP}) \tag{2}\]  
\[\text{Accuracy}=(\text{TN}+\text{TP})/(\text{TP}+\text{TN}+\text{FP}+\text{ FN}) \tag{3}\]  
\[\text{Recall}=\text{TP}/(\text{TP}+\text{FN}) \tag{4}\]  
It is important to note that these metrics are not perfect and have limitations. For example, specificity and recall are affected by the threshold value used for classifying pixels as tumor or non-tumor. Moreover, these metrics do not account for the size or location of the tumors, which could impact the clinical usefulness of the segmentation results. Therefore, it is important to use these metrics in combination with visual inspection of the segmentation results and feedback from medical professionals to ensure the clinical relevance of the model [7; 47; 69]. The system was developed using the Python programming language, which is widely used for machine learning and deep learning applications. The implementation was performed on a 64-bit operating system installed on a high-performance Intel 3.2 GHz-Core I7 computer. The use of a high-performance computer allowed us to process a large number of medical images with exceptional accuracy and efficiency. Python is a popular programming language for machine learning and deep learning due to its simplicity, ease of use, and large number of libraries and frameworks available for various tasks. The Python ecosystem also provides a variety of tools for data processing and visualization, making it a convenient choice for medical image analysis. The use of a 64-bit operating system provided several advantages over a 32-bit operating system, including the ability to address more memory and better performance for computationally intensive tasks. This allowed us to work with larger medical images and process them more efficiently, leading to better results.