## 4 Experimental Outcomes and Evaluations
### Evaluation Parameters
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Scholars’ Work** & **Applied Machine Learning Model** & **Description of Method** & **Applied Dataset** & **Performance Analysis** & **Advantage and Its Limitation** \\ \hline \multirow{2}{*}{[PERSON], [PERSON] et al. [9]} & \multirow{2}{*}{AM-Unet model} & Automated system using AIM-Unet model for better result. & CHAOS, LIST, and 3 DHCADe & DC = 97.86\% & The accuracy is remarkable as compared to DC and F1 score = 99.1\% & F1 score values. \\ \cline{2-6}  & & Using LITSI7 with the help of U-Net model yielding the better performance. & LITSI7 dataset & DC = 91.1\% & Based on the LITSI7 dataset U-Net approach the 15 score = 99.4\% & used method was able to obtain a higher accuracy in DC, mean, F1 score, and recall value. \\ \hline \multirow{2}{*}{[PERSON], [PERSON] et al. [17]} & \multirow{2}{*}{DL+ DAP} & To enhance the precision of CT liver segmentation, a semi-supervised adversarial learning model was trained using DAP. & LITS & DC = 94.33\% & A semi-supervised model that uses a training dataset lacking in annotations to reduce the amount of medical picture annotation that is necessary \\ \cline{2-6}  & & Two features are shared throughout the segmentation path and two inputs are sent into CoFCNNs simultaneously. & LITS & DC = 95.13\% & Utilizes a subset of the dataset, rather than the whole dataset. \\ \hline \multirow{2}{*}{[PERSON] et al. [20]} & \multirow{2}{*}{DeepMedic+CNN} & \multirow{2}{*}{DeepMedic+CNN} & Liver multi-scale segmentation utilizing DeepMedic CNN fusion of completely connected layers & SLIVER07 + LITS & DC = 93\% & The use of CNN segmentation as a starting point resulted in a decrease in inter-observer variation. \\ \hline \multirow{2}{*}{[PERSON] et al. [21]} & \multirow{2}{*}{Hybrid CNN} & Automated liver segmentation employing a 3D CNN with a hybrid loss function. & CHAOS & DC = 82.02\% & It is possible to train on tiny datasets, but the outcome will be average. \\ \cline{2-6}  & & Automated identification and labeling of liver functional areas using a 3D DNN with different classes. & \multirow{2}{*}{MSDC-T8} & \multirow{2}{*}{DC = 89.2\%} & There is a considerable computational cost associated with successful segmentation, and more research on different datasets is required. \\ \hline \multirow{4}{*}{[PERSON], [PERSON] et al. [30]} & \multirow{2}{*}{3 DPCNN} & A 3D Fully Convolutional Network & \multirow{2}{*}{Pancreas+CT} & DC = 92.93\% & An early effort to use divided CNN for liver segmentation and it requires post-processing. \\ \cline{1-1} \cline{4-6}  & & & & & \\ \cline{1-1} \cline{4-6}  & & & & & \\ \cline{1-1} \cline{4-6}  & & & & & \\ \cline{1-1} \cline{4-6}  & & & & & \\ \hline \multirow{2}{*}{[PERSON] et al. [31]} & \multirow{2}{*}{FRCM} & Application of FKPCM to segment liver and tumors & SLIVER07 & DC = 86.42\% & Significant outcomes are achieved while using unsupervised learning. \\ \cline{1-1} \cline{4-6}  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance analysis of different researchers with their advantageous techniques and limitations. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Scholars’ Work** & **Applied Machine Learning Model** & **Description of Method** & **Applied Dataset** & **Performance Analysis** & **Advantage and Its Limitation** \\ \hline [PERSON]. et al. [32] & U-Net + MSA & U-Net architecture and MSA & HSI dataset & DC = 81.3\% F1 score = 90.8\% F1 score = 90.8\% F1 score = 91.3\% F1 score = 92.2\% F1 score = 92.3\% F1 score = 92  
In our evaluation process as shown in Table 4, we used parameters such as accuracy, precision, recall, F1 score, and AUC-ROC. The applied models are CNN, RNN, LSTM, and our proposed model, CNN+LSTM. Accuracy, precision, recall, F1 score, and AUC-ROC are widely used metrics for assessment in machine learning and are very appropriate for evaluating the performance of classification models.