## 1 Introduction
It is critical to use deep learning to aid doctors in diagnosis. The poor accuracy of tumor classification, the limited capability of feature extraction, and the sparse dataset remain challenges in the current medical image classification task [12]. In 2018, [PERSON] et al. [13] developed a Watershed Gaussian Deep Learning algorithm for classifying three forms of liver cancer, including hepatocellular carcinoma, hemangioma, and metastatic carcinoma, utilizing 225 images. The watershed algorithm was utilized to segregate the liver, Gaussian Mixture Models (GMM) were utilized to detect the lesion region, and retrieved characteristics were fed into a Deep Neural Network (DNN). They were able to obtain 97.72% specificity, 100% sensitivity, and 98.38% testing accuracy. Consequently, [PERSON] et al. [14] trained a Convolutional Neural Network (CNN) to distinguish liver lesions into five categories utilizing 1068 images taken in 2013 from 460 patients and enhanced them by a factor of 52. Note that three max-pooling layers, six convolutional layers, and three fully connected layers made up the CNN. They had a median accuracy of 84% and a 92% Area Under the Curve (AUC). Moreover, [PERSON] et al. [15] utilized the LiTS dataset to segment the liver, utilizing the Morphological Snake method, and predicted the liver centroid utilizing an Artificial Neural Network (ANN). They obtained a 98.11% accuracy, 88% Dice Index, and 87.71% F1-score utilizing the LiTS dataset. Furthermore, [PERSON] [16] employed SegNet-UNet-BCO and LeNet5-BCO combinations to segment and categorize liver lesions in 2020, combining bio-inspired concepts with deep learning models. The models were trained to utilize the Radiopaedia and LiTS datasets, and the LiTS dataset yielded a 97.6% F1-score, 98.2% specificity, 97% Dice Index, 96.4% Jaccard Index, and 98.5% accuracy. To identify liver tumorsautomatically, [PERSON] et al. [17] utilized the ResUNet, a hybrid UNet and ResNet framework. Relying on the publicly accessible 3D-IRCADb01 dataset, they were able to attain a 99% accuracy and a 95% F1-score. In addition, [PERSON] et al. [18] presented a multi-scale and multimodal structure in 2021, utilizing a hierarchical CNN to automatically detect and categorize focal liver lesions. Following binary class discrimination, the model produced six classes. They attained an average accuracy of 82.5% in discriminating malignant and benign tumors utilizing 3D ResNet-18 and 73.4% in solving the six classes issue. Consequently, [PERSON] et al. [19] presented a comprehensive framework for separating cancerous and non-cancerous lesions in 2022. The framework is divided into three phases: liver segmentation, tumor segmentation, and lesion classification with an SVM classifier. They employed a combination of textual and statistical elements to analyze the LiTS17, MICCAI-Silver07, and 3 Dircadb liver datasets. The LiTS17 dataset obtained 95.57% accuracy, 96.23% sensitivity, 95.83% specificity, and 98.2% AUC, while the 3 Dircadb dataset achieved 96.88% accuracy, 97.32% sensitivity, 97.65% specificity, and 98.64% AUC. [PERSON] et al. [20] introduced a revolutionary, fully automated system for liver tumor classification, which employs computer vision and machine learning. A Gabor filter was employed to denoise the images, and the Correlation-based Feature Selection (CFS) approach was employed to maximize the features. On a 17 \(\times\) 17 Region of Interest (ROI), they obtained 97.48% accuracy using Random Forest and 97.08% accuracy utilizing Random Trees. According to the literature review, the subject of medical imaging is becoming more important as the demand for a precise and efficient diagnosis in a short amount of time grows. The liver serves a variety of activities, including vascular, metabolic, secretory, and excretory. CT is a medical imaging method that doctors can use to examine pathological abnormalities in the liver. The fundamental issue with liver segmentation from CT images is the poor contrast between the intensities of the liver and adjacent organs.