# Enhancing 3D Lung Infection Segmentation with 2D U-Shaped Deep Learning Variants
## 5 Conclusions  
In conclusion, this study delved into the potential of 2D architectures--specifically UNet, LinkNet, Attention UNet, UNet 3+, and TransUNet--for reconstructing 3D lung projections in the context of diagnosing lung infections, particularly in COVID-19 patients. The evaluation of these diverse architectures provided invaluable insights into their respective performances and effectiveness in accurately capturing lung structures and infection areas. This endeavor contributes to the advancement of medical image analysis, offering essential guidance to healthcare professionals and researchers in choosing appropriate techniques for assessing disease severity and monitoring its progression. Moreover, the practical implications extend to the development of more efficient clinical protocols for lung diseases. Our experimentation revealed that each architecture possesses distinct traits that impact its performance. UNet demonstrated remarkable results, achieving a dice score of 91.07% and an IoU score of 84.82%. Its foundational U-shaped architecture, which strikes a balance between contextual understanding and local details, proved to be effective in accurately segmenting lung structures. LinkNet, on the other hand, exhibited slightly lower performance, with a dice score of 86.71% and an IoU score of 79.31%. While its computational efficiency is notable. Attention UNet showcased its capability to focus on salient features by leveraging attention mechanisms, resulting in the highest performance with an IoU score of 85.36% and a dice score of 91.49%. UNet 3+ exploited full-scale skip connections and hierarchical feature fusion, yielding an IoU score of 84.77% and a dice score of 91.02%. TransUNet harnessed Transformers to capture global context, achieving an IoU score of 83.52% and a dice score of 90.27%. Although TransUNet required more epochs to converge due to its trait of integrating long-range dependencies, it still exhibited competitive performance. Considering the results and the scope of our study, there are potential future developments and research directions that can be explored. Investigating the generalizability and robustness of these architectures by testing them on larger and more diverse datasets would be valuable. Assessing their performance on CT-scans from different populations and varying disease stages can help to identify any limitations or potential biases in the models' segmentation capabilities. In summary, this study underscores the significance of these 2D architectures in visualizing and understanding lung constructions using CT-scans. These findings provide valuable insights for the development of advanced segmentation models and pave the way for further advancements in accurately visualizing and understanding the lung construction using CT-scans. **Author Contributions:** [PERSON], [PERSON], [PERSON], [PERSON] and T.H. comprehended and devised the research plan; [PERSON] and [PERSON] conducted data analysis and composed the initial draft. The draft was then reviewed and revised by all authors, who subsequently granted their approval for the final version of the paper. All authors have read and agreed to the published version of the manuscript. **Funding:** The authors would like to thank Higher Education Research Grant No. 094/E5/FG.02.00.PT/2022 for supporting this research, as well as the Universitas Padjadjaran and the Directorate for Research and Community Service (DRPM) Ministry of Research, Technology, and Higher Education Indonesia. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data presented in this study are openly available in [Zenodo] at [[[https://doi.org/10.5281/zenodo.375476](https://doi.org/10.5281/zenodo.375476)]([https://doi.org/10.5281/zenodo.375476](https://doi.org/10.5281/zenodo.375476))] (accessed on 18 November 2022).