## 3 Results and Discussion  
For this study, four ML classifiers named multilayer perceptron (MLP), support vector machine (SVM), random forest (RF), and J48 were deployed on selected optimized hybrid features (using cross-validation 10-folds) for the classification of liver cancer. There are many ML classifiers, but these four ML classifiers perform the best in the senses of accuracy and time in this study. As discussed earlier, four types of texture features, namely, the histogram, wavelet, co-occurrence, and run-length features, were extracted using the MR and CT-scan image dataset, and we fused these later on to generate a fused hybrid-feature dataset. First, experimentation was performed on an MRI-based hybrid-feature dataset (using 10-fold cross-validation), which did not give promising results on employed classifiers. The overall classification accuracy of the employed classifiers of MLP, SVM, RF, and J48 was 95.88%, 95.78%, 94.44%, and 94.44%, respectively. In the second step, the same approach was employed on the CT-scan-based hybrid-feature dataset. The overall classification accuracy of employed classifiers MLP, SVM, RF, and J48 was 97.44%, 96.89%, 96.83%, and 96%, respectively. There was a very promising improvement in the accuracy of the results as compared to that of the MRI-based dataset results. In this analysis, we observed that both CT and MRI modality have their own worth in liver cancer analysis and diagnosis. Thus, for further analysis, we fused the MRI and CT datasets using a data fusion approach to obtain more accurate classification results. Data fusion [38] is a very powerful technique for merging multiple datasets to produce an accurate classification as compared to individual datasets. As a final step, the same approach was employed on the overall classification accuracy of employed classifiers for fused optimized hybrid features. MLP, SVM, RF, and J48 showed a considerably higher classification accuracy of 99%, 98.5%, 98.17%, and 97.11%, respectively. The overall accuracy of the result of the MRI-based dataset with the employed ML classifiers was determined using other performance-evaluating factors, such as kappa statistics, which is a metric that compares an observed accuracy with an expected accuracy. This takes into account true positives (TP), which is an outcome where the model correctly predicts a positive class, false positives (FP), which is an outcome where the model incorrectly predicts a positive class, and precision, which is related to reproducibility and repeatability, defined as the degree to which measurements are repeated under unchanged conditions and given in Equation (32). \[Precision=TP/(TP+FP) \tag{32}\]  
Recall is the fraction of the total amount of relevant instances that were actually retrieved, given in Equation (33). \[Recall=TP/(TP+FN) \tag{33}\] \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Input Layers & 1 \\ Hidden Layers & 15 \\ Neurons & 18 \\ Learning Rate & 0.5 \\ Momentum & 0.4 \\ Validation Threshold & 18 \\ Epochs & 500 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Implemented multilayer perceptron (MLP) classifier constraints values. The f-score (or f-measure) is calculated based on the precision and recall, given in Equation (34). \[F-Measure=2\times Precision\times Recall\,/\left(Precision+Recall\right) \tag{34}\]  
The receiver-operating characteristic (ROC) is a graphical plot equating the TP rates and the FP rates of a classifier.