# Enhancing 3D Lung Infection Segmentation with 2D U-Shaped Deep Learning Variants
## 4 Results
### Result Discussion  
Using the diverse set of 2D segmentation architectures investigated in this study, we have achieved significant advancements in the reconstruction of 3D lung and infection projections from CT-scans. Each architecture demonstrates unique characteristics that contribute to their respective performances, enabling a comprehensive evaluation of their applicability in medical imaging tasks. Attention UNet stands out due to its sophisticated attention mechanisms that allow it to focus on relevant regions and features within the lung structures. This characteristic enhances its ability to capture intricate details specific to COVID-19-affected lungs. The attention mechanisms contribute to Attention UNet's impressive IoU score of 85.36% and dice score of 91.49%, showcasing its potential to provide accurate and finely detailed segmentations. The results suggest that its trait of dynamically assigning different weights to regions, based on their contextual importance, is instrumental in achieving precise and meaningful segmentations in complex medical images. UNet, characterized by its fundamental U-shaped architecture, adeptly strikes a balance between contextual information and local intricacies. This trait empowers it to attain an impressive IoU score of 84.82% and dice score of 91.07%, underscoring its proficiency in capturing both macroscopic patterns and intricate structural nuances. The consistent convergence observed in UNet could potentially be attributed to its inherent simplicity and directness in combining encoder and decoder features. This straightforward approach aids in achieving a holistic contextual understanding while simultaneously preserving intricate spatial details, ultimately contributing to its commendable performance. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Metrics** & **UNet** & **LinkNet** & **Attention UNet** & **Unet 3+** & **TransUNet** \\ \hline IoU score & 84.82\% & 79.31\% & **85.36\%** & 84.77\% & 83.52\% \\ Dice score & 91.07\% & 86.71\% & **91.49\%** & 91.02\% & 90.27\% \\ Accuracy & 98.58\% & 98.22\% & 98.63\% & 98.63\% & **98.96\%** \\ F1 score & 98.58\% & 98.23\% & **98.64\%** & 98.64\% & 98.33\% \\ Sensitivity & 98.58\% & 98.21\% & 98.63\% & 98.63\% & **99.81\%** \\ Specificity & 99.29\% & 99.12\% & **99.32\%** & 99.32\% & 98.41\% \\ Max Epoch 1 & 698 & **294** & 703 & 415 & 1539 \\ \hline \hline \end{tabular}  
* The Max Epoch is determined by reducing the total training epochs using the early stopping patience value. The bolded numbers in the table represent the best values in comparison to other architectures.  
\end{table} 
Table 5: Metrics evaluation of UNet, Linknet, Attention UNet, UNet 3+, and TransUNet. UNet 3+ demonstrates an IoU score of 84.77% and a dice score of 91.02%, showcasing its ability to effectively capture multi-scale features and generate hierarchical representations. This suggests that the architecture's unique traits play a significant role in its performance. The incorporation of full-scale skip connections and deep supervision within UNet 3+ enables it to seamlessly aggregate high-level semantics and intricate details. This approach empowers the model to maintain a harmonious balance between local and global information, ultimately enhancing its segmentation capabilities. TransUNet employs a distinctive approach by combining the capabilities of Transformers and UNet to effectively integrate global context and local spatial details. Despite requiring more training epochs compared to some other architectures, TransUNet achieves notable segmentation results. With an IoU score of 83.52% and a dice score of 90.27%, TransUNet's unique trait of harnessing Transformers for capturing long-range dependencies, in conjunction with UNet's spatial understanding, comes into play. This synergy \begin{table}
\begin{tabular}{c c c} \hline \hline
**Comparison of Ground Truth and Model Predictions in 3D Projections and 2D Slice** \\ \hline \hline \end{tabular}
\end{table}