## 3 Results and Discussion
Graphical accuracy results are shown in Figure 9.  
Figure 8: The overall accuracy graph of the employed ML classifiers on the fused dataset. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Classifiers** & \begin{tabular}{c} **Kappa** \\ **Statistics** \\ \end{tabular} & \begin{tabular}{c} **TP** \\ **Rate** \\ \end{tabular} & \begin{tabular}{c} **FP** \\ **Rate** \\ \end{tabular} & \begin{tabular}{c} **Recall** \\ **F-Measure** \\ \end{tabular} & \begin{tabular}{c} **ROC** \\ \end{tabular} & \begin{tabular}{c} **MAE** \\ \end{tabular} & \
\begin{tabular}{c} **RMSE** \\ **(s)** \\ \end{tabular} &
\begin{tabular}{c} **Trime** \\ **Precision** \\ \end{tabular} \\ \hline MLP & 0.988 & 0.99 & 0.002 & 0.99 & 0.99 & 0.997 & 0.2225 & 0.3108 & 0.31 & 0.99 \\ SVM & 0.982 & 0.985 & 0.003 & 0.985 & 0.985 & 0.999 & 0.0116 & 0.0655 & 0.19 & 0.985 \\ RF & 0.978 & 0.982 & 0.004 & 0.982 & 0.982 & 0.999 & 0.0114 & 0.068 & 0.28 & 0.982 \\ J48 & 0.9653 & 0.971 & 0.006 & 0.971 & 0.971 & 0.997 & 0.01 & 0.0933 & 0.07 & 0.972 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The overall classification accuracy table of the employed ML classifiers on the fused dataset. Figure 7: The overall accuracy graph of the employed ML classifiers on the CT-scan dataset. Finally, we present a comparative liver cancer classification graph of the MRI, CT-scan, and fused optimized hybrid-feature dataset using the employed ML classifiers. This graph shows an overall better accuracy (red) for liver cancer classification using the fused dataset as compared to the CT-scan (green) and MRI (blue)-based datasets, as shown in Figure 10. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Classified as** & **Hepatoblastoma** & **Cyst** & **Hemangioma** & **Adenoma** & **Carcinoma** & **Metastasis** & **Total** \\ \hline
**Hepatoblastoma** & **3588** & 2 & 0 & 8 & 0 & 2 & **3600** \\
**Cyst** & 4 & **3576** & 0 & 0 & 5 & 15 & **3600** \\
**Hemangioma** & 10 & 20 & **3540** & 5 & 25 & 0 & **3600** \\
**Adenoma** & 0 & 0 & 0 & 3590 & 5 & 5 & **3600** \\
**Carcinoma** & 10 & 30 & 5 & 45 & **3503** & **8** & **3600** \\
**Metastasis** & **2** & 0 & 10 & 0 & 0 & **3588** & **3600** \\ \hline \end{tabular}
\end{table} 
Table 6: Confusion matrix of the fused optimized hybrid-feature dataset using the MLP classifier. Figure 10: Comparative analysis of the liver cancer classification accuracy graph among different modalities’ datasets. Figure 9: Classification accuracy graph of the six liver cancer types using the MLP classifier on the fused optimized dataset. The existing system has some limitations because it is a supervised learning-based classifier. All experiments were performed on a liver cancer fused dataset, acquired to meet all legal requirements of the radiology department of the Bahawal Victoria Hospital (BVH) Bahawalpur Pakistan [21], with the collaboration of the Department of Computer Science of the Islamic University from Bahawalpur (IUB) [39], Pakistan to address the regional and local problem of identifying liver cancer types. This proposed model has been verified on the publicly available dataset of the liver cancer archives, the Radiopaedia Image Database [40]. Sixty liver cancer fused datasets of six liver types of liver, namely, hepatocellular adenoma, hemangioma, cyst, hepatocellular carcinoma, hepatoblastoma, and metastasis, totaling 360 (\(60\times 6\)) datasets, were acquired from the Radiopaedia public archive. Multi-institutional dataset differences between BVH and Radiopaedia were observed, and we tried our best to normalize the Radiopaedia dataset with respect to the BVH dataset.