## 4 Results and Discussion  
Figure 6 shows the sample images generated by the generative adversarial networks. Benign nodules are more round in shape compared to malignant images generated with spicula and withdrawal into the pleura. The images generated by GAN cannot be differentiated from the original images, even by a trained radiologist. This proves the importance of images generated by GAN.  
Nevertheless, the generated images are low resolution 64 \(\times\) 64 in size. GAN is able to generate 1024 \(\times\) 1024 resolutions for natural images. The improvement in GAN-based architectures will improve the resolution of the medical images, which will further enhance the accuracy in the detection and segmentation of lung nodules. Top row of the Figure 7 represents the SegNet segmentation on pulmonary nodules for benign cases along with its ground truth counterpart. Since the benign nodules are more round in shape, more accurate segmentation is possible compared to malignant cases, as is shown in Figure 7, bottom row In malignant cases, the nodules extend to the lung bones, and it is difficult to differentiate the difference. This reduces the segmentation accuracy considerably whencompared to benign nodules.  
Different methods and the performance results are tabulated in Table 2. It is evident from the table that GAN along with image augmentation was clearly the winner. Simultaneously, GAN with SegNet showed very small deviation from the winner. This clearly shows the advantage of using GAN for generating images for nodule segmentation. The method was also compared with other encoder-decoder architectures.  
The performance comparison is given in Table 3. Finally, the receiver operating characteristic (ROC) curve of SegNet while training the model with GAN and without  
Figure 6: Images generated by GAN. ((**a**) benign, (**b**) malignant).  
Figure 7: SegNet segmentation on benign images (**top** row) with ground truth representation (**bottom** row).  
GAN is given in Figure 8. The area under the curve (AUC) for SegNet + GAN was found to be 0.851, while that of SegNet was 0.76.  
Based on the proposed method and the methodologies employed, the effect of several design choices on the performance was analyzed. Here, the important factors that were analyzed, along with their corresponding impact on the performance of the model, are tabulated in Table 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Design Choice** & **Impact on Performance** \\ \hline \multirow{3}{*}{SegNet architecture} & Deeper model with more convolutional layers performed better. \\  & For example, SegNet-4 with 11 convolutional layers \\  & outperformed SegNet-2 with 7 convolutional layers. \\ \hline \multirow{3}{*}{Adversarial training} & Significant improvement in nodule segmentation accuracy. The \\  & proposed adversarial training method improved the mean dice \\  & coefficient for nodules from 0.717 to 0.773. \\ \hline \multirow{3}{*}{Pre-processing} & Lung cropping and normalization improved nodule segmentation \\  & accuracy. The mean dice coefficient for nodules increased from \\  & 0.717 to 0.743 after lung cropping and normalization. \\ \hline \multirow{3}{*}{Loss function} & Dice loss performed better than binary cross-entropy. The mean \\  & dice coefficient for nodules was higher with dice loss (0.773) than \\ \cline{1-1}  & with binary cross-entropy (0.752). \\ \hline \hline \end{tabular}
\end{table}
Table 4: Design choices and the impact on the performance of the system under investigation.  
Figure 8: ROC curve for proposed SegNet + GAN.