## 4 Results and Discussion  
Ultimately, by employing 253 photos and harnessing the computational capabilities of a 16 GB NVIDIA Tesla P100 graphics card, we utilized a generative network that operates on a random normal distribution to produce a sample that closely resembles the original distribution. For this network, we employed 12 epochs and 2000 steps for each epoch to generate images with a resolution of 128 by 128 pixels. The optimization algorithm we used was Adam, which had a learning rate of 0.0002. Table 4 displays the losses of the generating and discriminating functions at each stage. By graphing the distribution of the generated images in the network and comparing it to the distribution of real images in Figure 9, we can observe the overlap between these two graphs. This overlap indicates that the generated samples closely resemble the real images. For a better understanding, sample images of the initial stage and stage 12 of the network are shown in Figures 10 and 11, respectively. \begin{table}
\begin{tabular}{c c c} \hline
**EPOCH** & **Generator Loss** & **Discriminator Loss** \\ \hline
1 & 2.7882 & 0.2121 \\
2 & 1.7463 & 1.5576 \\
3 & 1.4884 & 0.7854 \\
4 & 2.0414 & 0.0719 \\
5 & 2.1013 & 0.2531 \\
6 & 4.2882 & 0.0856 \\
7 & 6.8006 & 0.0007 \\
8 & 4.5135 & 0.0913 \\
9 & 2.9847 & 0.1567 \\
10 & 7.7373 & 0.0208 \\
11 & 3.3118 & 0.0454 \\
12 & 2.7657 & 0.0614 \\ \hline \end{tabular}
\end{table} 
Table 4: The results of the value of the loss functions. Figure 9: Distribution of the original dataset with network-constructed data. Following the generation of new data and extraction of the mask for each image, it was time to assess the performance of the proposed deep neural network. Various evaluation criteria, such as accuracy, precision, sensitivity, and specificity, can be considered. Nevertheless, every measure possesses both benefits and drawbacks. Our strategy combines all of these aspects to attain exceptional outcomes. The F1 Score is a metric that quantifies the balance between precision and recall. It is defined by Equation (6), which considers both accuracy and sensitivity. Equation (7) demonstrates the Dice criterion (\(DSC\)), which is frequently used as a criterion in segmentation problems. The DSC criterion is frequently used to calculate the similarity between two pictures. The Dice coefficient measures the level of similarity between the segmentation model's outputs and the texture mask in image segmentation. Furthermore, with the Dice criterion, we utilize the Intersection-Over-Union (\(IoU\)) metric, also known as the Jaccard index. Equation (8) specifies the measure as the ratio of the overlapping area between the expected segmentation and the actual segmentation to the total area of the predicted segmentation and the actual segmentation. \[F1\ score=\frac{2\times(\text{precision}\times\text{sensitivity})}{\text{ precision}+\text{sensitivity}}=\frac{2 TP}{2\text{TP}+\text{FP}+\text{FN}} \tag{6}\]  
The confusion matrix assigns \(TP\) to true positives, \(FP\) to false positives, and \(FN\) to false negatives. \[DSC=\frac{2|X\cap Y|}{|X|+|Y|} \tag{7}\]  
\(X\) is the mask of each image, and \(Y\) is the mask predicted in the image by our model. \[IoU=\frac{|X\cap Y|}{|X\cup Y|} \tag{8}\]  
The segmentation network employed in our study utilized the Dice Similarity Coefficient (\(DSC\)) loss function and the Adam optimizer to segment MR images automatically. Furthermore, the data in this network undergoes group normalization to enhance the network's speed and stability. We had a grand total of 253 unique photos. The adversarial generating network generated ten additional images for each input image. Consequently, we possess a total of 2530 MR pictures, of which 80% (2024 images) were utilized for training, while 15% were allocated for validation. The dataset consisted of 379 photos, with the remaining 5% (127 images) reserved for testing the segmentation of deep neural networks. Figure 12 illustrates the proposed model's training and validation dice scores for 60 epochs.