## 1 Introduction
### Literature Review  
The Reference [11] proposed a study in which liver lesions were classified into malignant and benign using a comparative analysis. Fused fluorodeoxyglucose (FDG) PET/CT and MRI (PET/MRI)were compared with FDG PET/CT and MRI to find which among them is the best. Seventy patients were selected. A comparison was performed between the results of PET/MRI and those of PET/CT and MRI alone. All lesions were detected by MRI and fused PET/MRI, while PET/CT could detect 89.4% of them. The final accuracy obtained by PET/CT, MRI, and fused PET/CT and MRI was 66.7%, 80.0%, and 94.7%, respectively. The Reference [12] proposed a study on the development of an algorithm that uses deep learning for the detection and classification of liver lesions into malignant and benign. An algorithm was trained on a dataset containing 367 ultrasound (US) images. Then, the algorithm was tested using 117 new images. For the given model, the receiver operating characteristic-area under the curve (ROC-AUC) score for focal liver lesion FLL detection was 0.935, and the score for FLL classification was 0.916. The Reference [13] proposed a study that used dynamic contrast enhanced-magnetic resonance (DCE-MR) and T2-weighted images for automatic classification to get better results. In this study, 125 benign and 88 malignant lesions were used. Benign lesions included hemangiioma, adenoma, and cyst, while malignant lesions included metastasis and hepatocellular carcinoma. The gray-level co-occurrence matrix, gray-level histogram, and contrast curve texture features were taken out of the T2-weighted and DCE-MR images. Fifty features were selected to feed the tree classifier, which had the highest ANOVA F-score. Overall accuracy obtained was 0.77. The sensitivity/specificity was 0.62/0.77, 0.73/0.56, 0.84/0.82, 0.93/0.93, and 0.80/0.78 for metastasis, HCC, hemangiioma, cyst, and adenoma, respectively. The Reference [14] classified hepatic lesions as hemangiomas, cysts, and malignant cancers using feature selection methods based on multiple regions of interest (ROI). In this case, the classification of liver lesions was performed using ultrasound images. This depends to a large extent on certain properties such as internal edge, echogenicity, echo, morphology, and improvement of the subsequent echo. The proposed method achieved improved and stable classification, regardless of the characteristics used. The Reference [15] provided an overview of liver cancers in infants. Two of the most common cancers found in infants were hepatocellular carcinomas and hepatoblastomas. Recently, there has been much advancement in treatment outcomes. On the other side, hepatic cancers in infants are very rare. Four pediatric liver cancer study groups (international childhood liver tumor strategy group (SIOPEL), children oncology group (COG) and Japanese pediatric liver tumors group (JPLT)) joined together to work on these cancers. In this collaboration, a new histopathological agreement sorting of liver cancers in infants was established. Along with all this, some additions in chemotherapy, transplantations, and a web application were made. The Reference [16] introduced an end-to-end deep learning model to discriminate between liver metastases from colorectal cancer and benign cysts in CT images. This approach follows the InceptionV3 architecture and achieved a 96% accuracy result. The Reference [17] proposed an improved automatic classification of liver tumors on three-dimensional (3D) computed tomography (CT) volume images using fuzzy C-Means (FCM) and graph cuts. The liver volume of interest (VOI) was extracted and the region-growing algorithm was used to reduce computational cost and acquire promising results, reducing the processing time. The Reference [18] proposed mammogram segmentation was performed using second-order texture features. A clustering comparison was performed between fuzzy C-Means and K-Means algorithms. The segmentation results were measured in terms of error, such as mean square error (MSE) and root mean square error (RMSE).