## 1 Introduction  
Liver cancer (LC) is a well-known condition across the world. It is among the most frequent types of cancer that may affect humans [1]. It is a lethal disease spreading around the globe, particularly in underdeveloped nations [2]. The liver is the body's biggest internal organ. Hepatic cancer detection is difficult given the heterogeneous nature of liver tissues. The mortality rate of primary liver cancer can be reduced if it is detected earlier. For detecting the damaged region in liver images, multiple classification algorithms have been implemented [3]. The liver is both required for living and susceptible to a variety of diseases. CT examinations may be utilized to plan and deliver radiation treatment to tumors, as well as to assist biopsies and other less invasive procedures. Manual CT image segmentation and classification is a time-consuming and inefficient method, which is unfeasible for vast amounts of data. Manual interaction is not required with fully automatic and unsupervised approaches [4]. The computer-aided diagnosis of live tumors in CT images requires automatic tumor detection and segmentation. In low-contrast images, the low-level images are too faint to identify, making it a difficult process [5]. Tumor detection and segmentation are critical pre-treatment measures in the computer-aided diagnosis of liver tumors [6; 7]. In the liver, there are several different forms of tumors. The visual appearance of various tumors varies, and their visual appearance varies once the contrast medium is administered. Computer-aided diagnosis might be difficult when it comes to segmenting the liver from CT scan images accurately. Automatic liver segmentation is the initial and most important stage in the diagnosing process [8; 9]. Radiologists face a difficult problem in identifying and classifying liver tumors. The liver parenchyma must be separated from the abdomen, and the liver cells with the least alteration must be classified as malignant or benign tumors. Owing to its excellent cross-sectional view, outstanding spatial resolution, quick interpretation, and strong signal-to-noise ratio (SNR), CT images remain one of the top modalities of choice. Magnetic Resonance Imaging (MRI), Positron emission tomography (PET), and Ultra Sound (US) are the other major Liver-imaging modalities. CT examinations may be performed for proper planning and managing tumor treatments, including guiding biopsies and other easily established processes. For huge amounts of data, manual segmentation and Computed Axial Tomography (CAT) image categorization are demanding and time-consuming operations. Computer-aided diagnosis (CAD) systems are a type of medical imaging that acts as a second opinion for doctors when interpreting images. Upon creating the final output, the CAD systems are interactive/semi-automated and include the results of the medical practitioner. This contrasts with a fully automated system, wherein the computer software makes all choices. CAD systems have a critical role in the early diagnosis of liver disease, lowering the fatality rate from liver cancer [10]. The utilization of CT images to identify the liver disease is prevalent. Given the various intensities, it might be challenging for even competent radiologists to remark on the type, category, and level of the tumor immediately from the CT image. Designing and developing computer-assisted imaging techniques to aid physicians/doctors in enhancing their diagnoses has become increasingly significant in recent years [11]. The diagnosis and treatment strategy are determined by classifying the lesion type and time based on CT images, which demands professional knowledge and expertise to categorize. Once the workload is severe, fatigue is common, and even competent senior specialists have trouble preventing a misdiagnosis. Deep learning may overcome the limitations of conventional machine learning, for instance, the time required to retrieve image features and conduct dimensionality reduction manually, giving high-dimensional image features.