## 4 Conclusions  
This research work focused on the classification of six liver cancers (Hepatocellular adenoma, hemangioma, cyst, hepatocellular carcinoma, hepatoblastoma, and metastasis) with the help of texture analysis using a fused dataset based on the hybrid-feature analysis. The main goal was Otsu thresholding-based segmentation, selection of suitable optimized hybrid features, and the best classifiers for efficient classification. The variation of results is due to the different modalities of the MRI and CT-scan datasets. The fused hybrid-feature dataset was generated using a data fusion approach. Four machine learning classifiers, that is, MLP, SVM, RF, and J48, were employed using this optimized hybrid-feature dataset. The employed classifiers showed satisfactory results, but the MLP classifier results were exceptionally high among all other implemented classifiers. After implementing the MLP classifier, it was observed that an overall accuracy of 99% was achieved in these six liver tumors. The accuracies obtained by MLP on the six classes of hepatoblastoma, cyst, hemangiioma, hepatocellular adenoma, hepatocellular carcinoma, and metastasis were 99.67%, 99.33%, 98.33%, 99.67%, 97.33%, and 99.67% respectively. The proposed model has been verified on the publicly available dataset of Radiopaedia. A very promising result was observed, with a variation in classification accuracy of 95.94% to 98.27%. This research was designed in such a manner that if a new patient provides either a CT or a MR image, the system performs well. Our proposed system has the capability to verify the results on different MRI and CT-scan databases, which could help radiologists to diagnose liver tumors.  
Figure 12: Comparison accuracy graph between Bahawal Victoria Hospital (BVH) and Radiopaedia fused dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Source/Reference** & **Methodology** & **Modality** & **Accuracy** \\ \hline [PERSON] et al. [11] & Fused Features Based CT/PET/MRI & CT/MRI/PET & 94.7\% \\ [PERSON] et al. [12] & Deep Learning & US & 91.6\% \\ [PERSON], [PERSON] [13] & Texture + Tree Classifier & MRI & 77\% \\ [PERSON] et al. [14] & Internal Edge, Echogenicity, Echo, Morphology & US & 93.8\% \\ [PERSON] et al. [15] & SIOPEL/GPOH, COG, and JPLT & MRI & 92.5\% \\ [PERSON] et al. [16] & Inception V3 Modified Architecture + Deep Learning & CT & 96\% \\ [PERSON] et al. [17] & Fused Feature + Fuzzy C-Means & CT & 91.63\% \\ [PERSON] et al. [18] & Mammogram Segmentation & MRI & 86.67\% \\ [PERSON] [19] & Spatial and Temporal Features & US/CT & 91.8\% \\ \hline Proposed Methodology & \begin{tabular}{c} OTRGS + Optimized Fused Hybrid Features + MLP \\ \end{tabular} & \begin{tabular}{c} BVH (CT and MRI) \\ Dataset \\ \end{tabular} & 99\% \\ \hline Proposed Methodology (Validation) & \
\begin{tabular}{c} OTRGS + Optimized Fused Hybrid Features + MLP \\ \end{tabular} &
\begin{tabular}{c} Radiopaedia \\ (CT and MRI) \\ Public Dataset \\ \end{tabular} & 98.27\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison table of the proposed and current state-of-the-art techniques.  
It is a robust and efficient technique to reduce human error and can be implemented on large clinical datasets. The proposed system accurately diagnoses six types of liver tumors, including three benign and three malignant ones.