## 2 Related Work
In works [17; 19], the authors used the LiTS dataset and achieved a DC score of 94.33% and 95.13%, respectively. The scholars of [17] enhanced the precision of CT liver segmentation. A semi-supervised adversarial learning model was trained using DAP, and they applied a semi-supervised model that uses a training dataset lacking annotationsto reduce the number of medical picture annotations that are necessary. Whereas the researchers in [19] applied CoFCNN, the two features are shared throughout the segmentation path, and two inputs are sent into CoFCNNs simultaneously. The researchers in [20] applied DeepMedic+CNN. They used liver multi-scale segmentation utilizing DeepMedic CNN fusion of completely connected layers and achieved a DC value of 93% based on the SLIVER07 and LiTS datasets. Their use of CNN segmentation as a starting point resulted in a decrease in inter-observer variation. In work [21], a hybrid CNN was developed for automated liver segmentation employing a 3D CNN with a hybrid loss function and achieved a DC accuracy of 82.02% using the CHAOS dataset. However, there is the possibility to train on tiny datasets, but the outcome is average. The work in [24] used ResU-Net for the automated identification and labeling of liver functional areas using a 3D DNN with different classes. The authors used the MSDC-T8 dataset and achieved a DC accuracy of 89.2%. It was observed that there is a considerable computational cost associated with successful segmentation, and more research on different datasets is required. In work [30], a 3D Fully Convolutional Network (FCNN) Deep Learning (DL) technique was proposed for segmenting the liver, stomach, and pancreas utilizing divided convolution units. The authors showed an early effort to use divided CNN for liver segmentation, which requires post-processing. They showed that the DC value was 92.93% using the pancreas-CT. In the work [31] the authors used the FKPCM technique for segmenting livers and tumors. They used SLIVER07 and achieved a DC score of 86.42%, which shows that significant outcomes were achieved while using unsupervised learning. In the work [32] the authors used U-Net architecture and MSA, where the HSI dataset was applied, and they scored F1 score and DC values of 90.8% and 81.3%, respectively. They used the U-Net model and had remarkable precision in detecting liver, duct, vein, and artery tissues. In contrast, the accuracy of logistic regression with elastic net periodicity and multi-scale spatial analysis was comparatively lower. In work [33], the Mask R-CNN + GAN with KNN architecture was applied. The authors used the IRCADB01 dataset and gained the accuracy of recognition (ACC), recall, and F1 scores of 91.3%, 92.2%, and 92.3%, respectively. The applied Mask-R-CNN + GAN enhanced the pixels for better classification. The aspect's image ratio was modified using k-NN in order to obtain more prominent anchors, hence improving the segmentation's efficacy. In the work [34] the authors applied hybrid ResNet and UNet architectures. The dataset IRCADB01 was used, and they gained ACC, DC, and F1 scores with results of 99.55%, 97.85%, and 98.16%, respectively. This model deals with CT images of liver diseases using the IRCADB01 dataset. The identification accuracy proves that it can easily be used to find out about any type of unexpected thing in the liver. In the work [35] the scholars applied a cascaded fully convolutional neural network in the ACC, DC, and F1 scores. The accuracy of segmentations and dice coefficient values indicated the detection, and its accuracy was significantly higher compared to other researchers. The researchers [36] applied AlexNet with multi-layer GAN for liver diagnosis. In their research experiment, they applied three publicly available datasets: Ircadb, Sliver07, and LiTS. In this regard, to achieve a better result, they adopted binary classification and achieved DC and F1 scores of 90.37% and 85.90%, respectively.