## 3 Results and Discussion
In the next stage ResNet50 and LiverNet are employed as feature extractors In both networks, the two features are retrieved from the final fully connected layer. Finally, the labeled data are classified using gaussian SVM. The model is built utilizing a Gaussian SVM classifier to distinguish between malignant and benign tumors. Figure 10 describes the confusion matrix of the Gaussian SVM using 3D graphical features of ResNet50. Here, the total accuracy reached 97%. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Net Work** & **Train** & **Test** \\ \hline ResNet50 & 123 min & 32 s \\ Proposed model & 89 min & 22 s \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison for training and testing time for both ResNet50 and LiverNet. Figure 9: AROC curve using LiverNet. The first two diagonal cells in Figure 7 reflect the number and percentage of correct classifications in ResNet50 utilizing a Gaussian SVM with 3D graphical features. The benign classification for the 22 cases was valid, representing 45.8% of the total 48 cases. In the same approach, 25 cases were accurately identified as malignant, which was 52.1% of the total number of cases. One of the benign occurrences was mistakenly labeled as malignant, accounting for 2.1% of the total 48 cases in the study. From 22 benign predictions, it was stated that 100% were correct; meanwhile, from 26 malignant predictions, 96.2% were revealed to be correct. Furthermore, from 23 benign cases, 95.7% of them were correctly predicted as benign, meanwhile, from 25 malignant cases, 100% were correctly categorized as malignant. In total, 97.9% of the predictions were revealed to be correct, while 2.1% of them were wrong. Figure 11 represents the ROC curve of the hybrid system using a pretrained CNN. Figure 10: Test Confusion matrix of SVM with features from ResNet50. The first two diagonal cells in Figure 12 reflect the number and percentage of correct classifications utilizing the suggested net's gaussian SVM with 3D graphical features. For example, the benign classification for the 23 occurrences was correct, representing 47.9% of the total 48 occurrences. In the same approach, 25 occurrences were accurately identified as malignant, which was 52.1% of the total number of cases. Figure 11: AROC curve of SVM with features from ResNet50. Figure 12: Test Confusion matrix of SVM with features from LiverNet. Of 23 benign cases, 100% of them were correctly predicted as benign, meanwhile, from 25 malignant cases, 100% were found to be correctly categorized as malignant. Overall, 100% of the predictions were correct. Figure 13 shows the ROC curve of the hybrid system using LiverNet features. The evaluation criteria that have been used in this paper are clear in the corresponding equations [30]. Table 5 describes the results in the deep learning track and hybrid track for both ResNet50 and the proposed LiverNet. The following equations are used to calculate the performance of the classifier [30]. \[\text{Sensitivity}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{2}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{3}\]  
\[\text{Specificity}=\frac{\text{TN}}{\text{TN}+\text{FP}} \tag{4}\]  
\[\text{Accuracy}=\frac{\text{TP}}{\text{TP}+\text{TN}+\text{FP}+\text{FN}} \tag{5}\]  
where TP is True Positive, TN is True Negative, FP is False Positive, and FN is False Negative. The high performance of the proposed net is clear as a feature extractor. Furthermore, Figure 14 below illustrates the high performance of the proposed approach in obtaining an accurate diagnosis of liver tumors. Figure 13: AROC of hybrid system with features from LiverNet.