# Segmentation of Liver Tumor in CT Scan Using ResU-Net
## 5 Conclusions  
In this work, we utilized the ResU-Net model to automatically segment the liver and its tumors into pixels from the liver CT scan images. The proposed technique is one of the best techniques that can be used in the segmentation of the liver and its tumors. It can also be used even though this technique can enhance the result for other research work related to tumors like lung and brain tumors, etc. Researchers are working on a performance-based study of various networks specifically employed for liver and tumours. A wide range of data sets can be counted and used extensively by testing and implementing this approach and enhancing the result time by time. The proposed ResU-Net network in this study outperforms state-of-the-art techniques in the liver and its tumor segmentation.  
Future WorkThe deep learning-based techniques can improve performance when using large numbers of liver datasets. Additionally, data preparation techniques such as the generative adversarial network (GAN) can be utilised to increase the size of liver images. Although the ResU-Net demonstrated promising results, the authors should try alternative deep learning-based networks in the future to see if they can increase performance further.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Approach** & **DSC** & **Accuracy** & **Precision** & **Specificity** & **VOE** & **RVD** \\ \hline [PERSON] et al. [13] & 0.823 & - & - & - & - & - \\ \hline [PERSON] et al. [39] & - & - & - & - & 15.6 & 5.8 \\ \hline [PERSON] et al. [38] & 0.83 & - & - & - & 29.04 & \(-\)2.20 \\ \hline [PERSON] et al. [24] & 0.595 & - & - & - & 0.380 & \(-\)0.152 \\ \hline [PERSON] et al. [37] & 0.67 & - & - & - & - & 0.40 \\ \hline [PERSON] et al. [25] & 0.74 & 0.995 & - & - & - & \\ \hline
**Proposed ResU-Net** & **0.893** & **0.97** & **0.950** & **0.957** & **13.15** & **7.23** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Result comparison of ResU-Net based liver tumor segmentation with state-of-art techniques.  
**Author Contributions:** [PERSON] and [PERSON] devised the project, the main conceptual ideas, and the proof outline, as well as planned and supervised the work; [PERSON] and [PERSON] contributed to data preprocessing, investigation, training, tested the algorithm, and wrote the original manuscript; [PERSON] worked on the manuscript to further improve the outline, technical details and result analysis. [PERSON], [PERSON], [PERSON], [PERSON], S.S.A.A. and [PERSON] contributed to the design and implementation of the research, to the analysis of the results; [PERSON]-[PERSON], [PERSON], [PERSON] and [PERSON] reviewed the manuscript and provided valuable suggestions to further refine the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research is supported by YUTP-FRG funded by PRF under Grant YUTP 015 LC0-235.  
Not Applicable.  
The datasets used in this work are public and can be found at [[https://www.ircad.fr/research/data-sets/](https://www.ircad.fr/research/data-sets/)]([https://www.ircad.fr/research/data-sets/](https://www.ircad.fr/research/data-sets/)).  
This work received funding from the YUTP-FRG by PRF under Grant number YUTP 015 LC0-235.  
The authors declare no conflict of interest.