## 3 Results and Discussion
As the refinement threshold of the classifier is different, the mean absolute error (MAE) quantity, used to measure how close forecasts or predictions are to the eventual outcomes, and the root mean squared error (RMSE), which is the sample standard deviation of the differences between predicted values and observed values, as well as the confusion matrix and time complexity (T) are shown in Table 3. It has been observed that in the employed classifiers on the MRI-based dataset, the multilayer perceptron (MLP) classifier showed a relatively better classification accuracy of 95.88% as compared to the other deployed classifiers shown in Figure 6.  
To acquire some more promising improvements in classification accuracy, we then deployed the same classifiers on the CT-scan-based dataset shown in Table 4.  
It has been observed that the overall accuracy of MLP, SVM, RF, and J48 was 97.44%, 96.89%, 96.83%, and 96%, respectively, as shown in Figure 7. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Classifiers** & **Kappa** & **TP** & **FP** & **ROC** & **Recall** & **F-Measure** & **MAE** & **RMSE** & **Time** & **Precision** \\ \hline MLP & 0.9693 & 0.974 & 0.005 & 0.995 & 0.974 & 0.975 & 0.2228 & 0.3113 & 0.27 & 0.976 \\ SVM & 0.9627 & 0.969 & 0.006 & 0.998 & 0.969 & 0.969 & 0.0176 & 0.0874 & 0.12 & 0.969 \\ RF & 0.9367 & 0.968 & 0.032 & 0.997 & 0.968 & 0.968 & 0.0302 & 0.1542 & 0.11 & 0.968 \\ J48 & 0.952 & 0.960 & 0.008 & 0.996 & 0.960 & 0.960 & 0.0334 & 0.1203 & 0.09 & 0.960 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The overall classification accuracy table of employed ML classifiers on the liver cancer CT-scan dataset. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Classifiers** & **Kappa** & **TP** & **FP** & **ROC** & **Recall** & **F-Measure** & **MAE** & **RMSE** & **Time** & **Precision** \\ \hline MLP & 0.9507 & 0.959 & 0.008 & 0.979 & 0.959 & 0.959 & 0.0154 & 0.1152 & 0.42 & 0.959 \\ SVM & 0.9493 & 0.958 & 0.008 & 0.996 & 0.958 & 0.958 & 0.0276 & 0.1083 & 0.31 & 0.958 \\ RF & 0.9333 & 0.944 & 0.011 & 0.986 & 0.944 & 0.944 & 0.2236 & 0.3125 & 0.11 & 0.946 \\ J48 & 0.9333 & 0.944 & 0.011 & 0.995 & 0.944 & 0.944 & 0.0295 & 0.1198 & 0.16 & 0.944 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The overall classification accuracy table of employed machine-learning (ML) classifiers on the liver cancer magnetic resonance imaging (MRI) dataset. Figure 6: The overall accuracy graph of the employed ML classifiers on the MRI dataset. Finally, the overall accuracy of the MRI and CT-scan dataset was not so impressive, while regarding the fused optimized hybrid feature, the same strategy with the same deployed classifiers was employed for this image dataset of six liver cancers, and we observed very promising results, with accuracy levels between 97.11% and 99%. The overall classification accuracy of the employed classifiers, that is, MLP, SVM, RF, and J48, was 99%, 98.5%, 98.17%, and 97.11%, respectively, as shown in Figure 8.  
These results were encouraging, and we observed that the MLP showed the best accuracy among all the implemented classifiers. The four implemented ML classifiers on the fused optimized hybrid-feature dataset gave the accuracy results shown in Table 5.  
Similarly, the confusion matrix (CM) of the fused optimized hybrid feature is shown in Table 6. The diagonal of the CM (TABLE) shows the classification accuracy in appropriate classes, while other instances show them in other classes. It contains the actual and predicted data for the MLP classifier. The MLP classifier showed a relatively better overall accuracy among the implemented classifiers. The classification accuracy of the results of the fused dataset of the six types of liver cancer, that is, hepatoblastoma, cyst, hemangioma, hepatocellular adenoma, hepatocellular carcinoma, and metastasis, was 99.67%, 99.33%, 98.33%, 99.72%, 97.30%, and 99.67%, respectively.