# A Novel Method for 3D Lung Tumor Reconstruction Using Generative Models
## 4 Empirical Evaluation
### Main Results
#### 4.3.3 Three-Dimensional Reconstruction  
In our examination of various 3D reconstruction models, we have conducted an in-depth analysis of six distinctive methods: YOLOv4 [20], GAN-LSTM-3D [22], GAN-GK-LSTM [55], GAN-ResNet-3D [52], ANN-GGO [53], alongside our proposed model. To further assess our model, we conducted ablation studies to evaluate the impact of various components: Proposed w/o VGG-16, Proposed w/o SP, Proposed with GANDA, and Proposed with AEDA. The Proposed w/o VGG-16 configuration eliminates the VGG-16 layer, allowing images to be fed directly into the LSTM. The Proposed w/o SP variant does not utilize stratified partitioning for hyperparameter tuning. Additionally, the Proposed with GANDA and Proposed with AEDA models incorporate GAN and autoencoder (AE) for data augmentation purposes. The evaluation results are shown in Table 4.  
Within the existing models, YOLOv4 takes the lead, showcasing the lowest values for HD at 1.146 and Euclidean Distance (ED) at 1.236, which serve as indicators of reconstruction accuracy. GAN-LSTM-3D trails just behind, with only marginally higher metrics. A more noticeable increase in ED is seen with GAN-GK-LSTM, particularly its rise to 1.494, signifying a decrease in volumetric precision. GAN-ResNet-3D and ANN-GGO, on the other hand, report the highest HD and ED values, with ANN-GGO deviating most substantially from the actual tumor shapes, as reflected by its HD of 2.630 and ED of 2.023. This diverse performance spectrum suggests that while some models approach the accuracy of our proposed model, there is room for improvement. The proposed model establishes a new standard, exhibiting HD and ED values of 0.986 and 1.126, respectively, thereby eclipsing the performance of all comparator models. Relative to YOLOv4, the nearest rival, our model demonstrates an improvement of approximately 13.9% in HD and 8.9% in ED. The advancements are even more striking against ANN-GGO--the model with the least accuracy among those compared--where we observe improvement rates of 62.5% in HD and 44.3% in ED. This pronounced differentiation emphasizes the refined capability of our model to delineate the complex structures of lung tumors with impressive precision. The Proposed w/o VGG-16 model showed a significant decrease in performance, where omitting VGG-16 and directly inputting images into LSTM led to increased HD and ED values of 1.342 and 1.438, respectively. This decrease highlights VGG-16's essential role in extracting image features, which affects the model's accuracy in 3D lung tumor reconstruction. VGG-16 crucially enhances the interpretation of complex medical images, which could be more effectively handled by the LSTM alone. Additionally, eliminating stratified partitioning from hyperparameter tuning resulted in minor performance reductions, with HD and ED values of 0.975 and 1.186, respectively. This reflects a slight decrease in  
Figure 5: Comparative analysis of tumor detection. The top row shows the ground truth, while the bottom row presents the model predictions. The black square in every sample shows the tumor extracted by the proposed model. performance, indicating that while stratified partitioning supports robust training through balanced class representation, its absence subtly affects precision but does not critically reduce overall model effectiveness. Incorporating GAN for data augmentation (Proposed with GANDA) resulted in HD and ED values close to the base model, at 0.983 and 1.132, demonstrating that GANs effectively replicate real data variations and support accurate tumor reconstruction. Conversely, using an autoencoder for data augmentation (Proposed with AEDA) resulted in HD and ED values of 1.035 and 1.205, decreasing performance. Although AEDA enhances data representation, it may not fully capture the critical nuances needed for optimal 3D tumor reconstruction. The statistical significance shows that the proposed model significantly outperforms the comparator models regarding HD and ED, with \(p\)-values consistently below 0.05 across all comparisons.