## Materials and Methods
### Proposed Methodology  
First, proposed algorithm is described with all the procedural steps in Algorithm 1. Then proposed methodology is described in Figure 2.  
```
Begin Main { Input \(\epsilon\) Liver cancer CT and MR image dataset For {  Step 1 to Step 9
1. MRI datasets \(\epsilon\) six liver cancer types
2. CT-scan datasets \(\epsilon\) six liver cancer types
3. Image preprocessing
4. Otsu thresholding-based region growing segmentation
5. Extract hybrid-feature \(\epsilon\) histogram, co-occurrence matrix, run-length matrix, and wavelet
6. Fused (MRI and CT-scan) hybrid-feature dataset
7. Hybrid-feature optimization via probability of error plus average correlation feature selection technique
8. Extract 10 optimized fused hybrid-feature dataset End For }
9. Machine-learning classifiers are employed on fused hybrid-feature dataset Output = Liver cancer classification results }
```  
**Algorithm 1** Otsu thresholding-based segmentation and fused hybrid-feature analysis for liver cancer classification proposed algorithm.  
Now, let us discuss the proposed methodology in detail. The first step consisted of a collection of image datasets with two categories of liver cancer. These two categories are (i) benign liver cancer, which includes a further three subcategories (hepatocellular adenoma, hemangioma, and cyst), and (ii) malignant liver cancer, which includes three subcategories (hepatocellular carcinoma, metastasis, and hepatoblastoma). In this step, for each type, 100 patients were selected to examine their liver cancer using a CT scan, and 100 patients were examined using MR images of a size of \(512\times 512\), and a dataset of \(1200\) (\(100\times 6\times 2\)) fused (MR and CT) images of a liver cancer patients was acquired from the radiology department of Bahawal Victoria Hospital Bahawalpur, Pakistan [20]. The second step is image preprocessing. In this step, firstly, digital MR and CT images were converted into a gray-level eight-bit image format. Secondly, noise removal was performed using \"Gabor filters\". Thirdly, we enhanced the images using \"Sharping Algorithm II\" to sharpen the edges of the image, then a data cleaning was done for liver cancer image dataset standardization. The third step was segmentation, which helped to remove the extra object, nominate the exact position, and refine the texture of the lesion. There are several automated and semiautomated methods for extracting ROI.  
Figure 2: Otsu thresholding-based segmentation and fused hybrid-feature analysis for liver cancer classification framework.  
Automated ROI extraction is generally based on the idea of image segmentation, but there is no single technique for ideal segmentation. On the other hand, there are semiautomated techniques based on expert opinion, but human-based extraction has some limitations. To solve this problem, Otsu thresholding-based region growing segmentation (OTRGS) was employed on the liver cancer fused image dataset. The segmentation process is shown in Figure 3. The fourth step was first-order and second-order hybrid-feature extraction. In this step, four types of features, namely, \"Co-Occurrence Matrix Feature\", \"Wavelet Feature\", \"Run-Length Matrix feature\", and \"Histogram Feature\", were extracted from the standardized liver cancer (MR and CT) image dataset. The fifth step was the formation of a fused hybrid-feature dataset using the data fusion technique. The sixth step was fused hybrid-feature optimization. In this step, we selected the best 10 optimized fused hybrid features of the extracted feature dataset using probability of error, plus the average correlation feature selection technique. The last step was classification, where four ML classifiers named multilayer perceptron (MLP), support vector machine (SVM), random forest (RF), and J48 were employed (using cross-validation 10) on a selected optimized fused hybrid-feature dataset.