## 2 Related Work
The report included an in-depth description of the utilized dataset, including clinical and image data. Moreover, an explanation of the pre-processing and feature selection methods was provided. It was anticipated that these results would be presented with a demonstration showcasing the performance of each machine learning algorithm, encompassing pertinent evaluation metrics such as accuracy, sensitivity, specificity, and more. In the study on the classification of liver cancer by combined CT scans and MR, researchers [15] proposed a hybrid-feature analysis that is based on machine learning. They created a model that improves the accuracy of liver cancer classification by combining features from both types of imaging modalities. The pre-processing steps, technique for feature extraction, and machine learning algorithm that were used were all described in the paper. It was anticipated that these findings would demonstrate that the proposed model possesses superior performance when compared to using each imaging modality individually with various metrics [16]. The work [17] developed a method for screening for liver disease that is based on tightly coupled deep neural networks. For the purpose of screening for liver disease, the authors made use of DL techniques, more specifically, densely connected neural networks. The pre-processing steps that were utilized, the dataset that was used for training and estimation, and the architecture of the deep neural network were all described in the paper. In addition to other pertinent performance metrics, their findings included a comparison of this approach with others, as well as a discussion of the accuracy of the model in screening for liver disease. The work [18] investigated the use of digital testing for pathology and ML in the context of kidney, lung, and liver diseases. The authors examined previously published studies and discussed the utilization of digital pathology images as well as ML techniques in the process of diagnosing these diseases. Findings from many studies were put together in this article, along with an analysis of the effects and possible benefits of using digital pathological testing and machine learning methods in the areas of kidney, lung, and liver diseases. The work [19] examined both the obstacles that must be overcome and the potential benefits of using DL in the diagnosis of liver disease. They provided an overview of the DL techniques, discussed the challenges that are faced in liver disease diagnosis, such as limited data or interpretability, and investigated potential future directions in this field. This paper provided perspectives on the potential benefits and limitations of using DL to diagnose liver disease and presented insights based on the literature review. In their study [20] the authors focus on the first detection and diagnosis of liver cancer using dynamic network biomarkers and DL. The authors suggested a new method that integrates dynamic network biomarkers with deep learning algorithms. The article includes a description of the methods used to find dynamic network biomarkers, as well as an explanation of the architecture of the DL model. Their results include the performance of the proposed approach. This encompasses the model's accuracy, sensitivity, specificity, and ability to identify liver cancer early. The study investigated by authors [21] present in-depth knowledge of the use of machine learning models in the clinical diagnosis of both primary and metastatic liver tumors. They provided a thorough analysis of the literature and a summary of the results from several studies that have used machine learning models to diagnose liver cancer. The study discusses the various machine learning techniques used, the datasets examined, and the stated diagnostic accuracy of the models. In the works [22; 23], a more advanced preprocessing technique was suggested for the detection of liver disease that utilizes collective machine learning techniques.