# A Novel Method for 3D Lung Tumor Reconstruction Using Generative Models
## 4 Empirical Evaluation
### Main Results
#### 4.3.2 Tumor Detection  
The proposed tumor detection model is compared with five models, DEHA-Net [64], DS-CMSF [65], DA-Net [66], R-CNN [67], PN-SAMPM [68], whose the results are shown Table 3. Within the competitive landscape, DEHA-Net sets a high standard with an IoU of 0.863, signifying a close match with the ground truth in tumor detection. DS-CMSF follows suit with an impressive IoU of 0.842. There is a notable drop in IoU for DA-Net, R-CNN, and PN-SAMPM, with scores of 0.790, 0.768, and 0.752, respectively. This descending trend might imply a diminishing accuracy in capturing the full extent of the tumors. Examining the precision of the tumor boundaries, DEHA-Net again takes the lead with the lowest HD of 0.726, which suggests that its predicted tumor edges are nearest to those in the actual medical images. The subsequent models, DS-CMSF, DA-Net, R-CNN, and PN-SAMPM, exhibit incrementally higher HD values, pointing to less precise boundary detections. The proposed model, however, outperforms all with an IoU of 0.901, surpassing DEHA-Net, the closest competitor, by 4.4%. This indicates that the proposed model detects the presence of tumors more accurately and provides a segmentation that significantly overlaps with the verified ground truth. Moreover, the proposed model achieves an HD of 0.625, an improvement of 13.9% over the best-performing existing model, DEHA-Net. This metric emphasizes the enhanced capability of the proposed model to delineate tumor boundaries with greater exactitude.  
The statistical significance shows that the proposed model substantially outperforms existing models in tumor detection, with \(p\)-values < 0.05 indicating strong statistical significance. For instance, the proposed model's IoU of 0.901 and HD of 0.625 compared favorably against the next best model, DEHA-Net, which recorded an IoU of 0.863 and HD of 0.726. The confidence intervals for these metrics further reinforce the superiority of the proposed model, illustrating robust improvements in both IoU and HD measures. These data substantiate the claim of our model's superiority in precise tumor detection.  
Figure 5 presents three pairs of images generated by the proposed tumor detection model, where the ground truth annotations are displayed in the top row, and the outputs of the model are shown in the bottom row. A collective analysis of these images suggests that the proposed model demonstrates a commendable level of precision in pinpointing the location of lung tumors. Across the set, the regions marked by the model align well with the ground truth indicators, denoted by squares. This alignment indicates that the model has successfully learned to identify the critical features of lung tumors from CT scans and can consistently distinguish tumor tissue from surrounding anatomical features.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **IoU** & **HD** \\ \hline DEHA-Net [64] & 0.863 & 0.726 \\ \hline DS-CMSF [65] & 0.842 & 0.769 \\ \hline DA-Net [66] & 0.790 & 0.871 \\ \hline R-CNN [67] & 0.768 & 1.206 \\ \hline PN-SAMPM [68] & 0.752 & 1.426 \\ \hline Proposed & 0.901 & 0.625 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance metrics of various deep learning algorithms for tumor detection on the LIDC-IDRI dataset.