## 3 Results and Discussion  
The data are divided into 70% training data. Meanwhile, the rest resembles testing. The transfer learning strategy is employed here to be suitable for two classes. The maximum accuracy obtained using ResNet50 is 83.7%. After taking 123 min in the training stages, Figure 6 illustrates the confusion matrix for ResNet50. \begin{table}
\begin{tabular}{c c} \hline \hline
**Layer** & **Information** \\ \hline Input Layer & Size [223 \(\times\) 223 \(\times\) 147] \\ \hline \multirow{4}{*}{Conv\_1} & Number of Filters 6 \\  & Kernel size 5 \(\times\) 5 \(\times\) 5 \\  & Stride 2 \(\times\) 2 \(\times\) 2 \\  & Padding 0 \\ \hline \multirow{4}{*}{Pooling Layer} & Type Average Pooling \\  & Kernel size 2 \(\times\) 2 \(\times\) 2 \\  & Stride 2 \(\times\) 2 \(\times\) 2 \\  & Padding 0 \\ \hline Activation Layer & ReLU \\ \hline Fully-connected Layer & 10 neurons \\ \hline Fully-connected Layer & 2 neurons \\ \hline Softmax Layer & \\ \hline Classification Layer & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Layers information for the proposed LiverNet. The number and percentage of correct classifications by the pre-trained network are shown in the first two diagonal cells of Figure 6. For instance, 23 occurrences were categorized as benign appropriately. This represents 46.9% of the total of 49 occurrences. In the same way, 18 occurrences were accurately labeled as malignant, in which 36.7% of all cases fell into this category. Eight of the malignant cases were misclassified as benign, accounting for 16.3% of the total 46 cases in the study. Likewise, 0 benign biopsies were wrongly labeled as malignant, accounting for 0% of the total data. From 31 benign predictions, 74.2% were found to be correct, meanwhile 25.8% were revealed to be wrong. Of 18 malignant predictions, 100% were correct, and 0% were wrong. Of 23 benign cases, 100% were revealed to be correctly predicted as benign, while 0% were predicted as malignant. Of 26 malignant cases, 69.2% were correctly classified as malignant, while 30.8% were categorized as benign. In total, 83.7% of the predictions were revealed to be correct, while 16.3% of them were wrong. The pertained network is very badly sensitive to malignant cases. Almost 31% of the malignant cases were diagnosed as benign, which is not acceptable in medical field applications. Figure 7 shows the ROC curve for this case. Figure 6: Test Confusion matrix of ResNet50. The proposed net obtained a high accuracy when compared with ResNet50, and Figure 8 shows the confusion matrix of the LiverNet. Here, the accuracy reached 95.9%. Figure 8: Test Confusion matrix of LiverNet. Figure 7: AROC curve using ResNet50. The number and percentage of correct classifications by the suggested network are shown in the first two diagonal cells of Figure 7. A total of 23 occurrences, for example, were accurately categorized as benign. This represents 46.9% of the total of 49 occurrences. In the same approach, 24 occurrences were accurately labeled as malignant. This was the case in 49% of all occurrences. Two of the malignant instances were mistakenly categorized as benign, accounting for 4.1% of the total 49 cases in the study. From 23 benign predictions, it was revealed that 100% were correct. Meanwhile, from 24 malignant predictions, 100% were found to be correct. Moreover, from 23 benign cases, 100% were correctly predicted as benign, meanwhile, from 24 malignant cases, 92.3% were correctly classified as malignant and were discovered to be 7.7% wrong. In total, 95.9% of the predictions were found to be correct, meanwhile 4.1% were shown to be wrong. The proposed network performance was better than the pre-trained CNN. Figure 9 illustrates the ROC curve of classification using LiverNet. The number of convolutional layers in LiverNet is one, which makes it fast in training and testing. Table 4 shows the time required for training and test phases for both the existing CNN and the proposed one.