## 2 Related Works  
This section reviews previous liver and tumor segmentation work from abdominal CT images, identifying major research gaps. Several studies have proposed diverse deep-learning models, achieving promising results using UNet and its variations, fully convolutional networks, attention mechanisms, graph-based techniques, and generative adversarial networks. The authors in [21] introduced the fully convolutional neural network (FCN), revolutionizing image segmentation by enabling end-to-end pixel-level output. Building on this approach, [PERSON] et al. [22] developed the UNet architecture, which incorporates an encoder-decoder structure, a U-shaped network, and skip connections for improved feature fusion. Several enhancements to the UNet architecture have since been made. [PERSON] et al. [23] developed an automated liver and tumor segmentation system using ResUNet on CT scans, combining UNet with residual connections and pre-processing techniques to enhance feature extraction. [PERSON] et al. [24] introduced attention UNet, incorporating a spatial attention mechanism to focus on regions of interest. [PERSON] et al. [25] redesigned UNet with skip connections, creating UNet++, a multi-scale network with dense connections at the exact resolution, to address the semantic gap in feature fusion. [PERSON] et al. [26] used UNet++ for liver and tumor segmentation in CT images, improving accuracy with deep supervision and hybrid loss functions. [PERSON] et al. [27] further enhanced UNet++ with full-scale skip connections and deep supervision, resulting in UNet3+, which improves accuracy and reduces the number of parameters. However, the complexity of these networks, with their numerous skip connections and deep supervision, may lead to overfitting. [PERSON] et al. [28] proposed DRAUNet, incorporating deep residual blocks and a dual-attention module to enhance feature representation and correlation modeling. Other notable approaches include PADLLS [29], a cascaded method combining V-net and H-DenseUNet for accurate liver segmentation on SLIVER07 and 3 DIRCADb datasets. ResCEAttUNet [30] integrates Attention UNet, a context encoder, and a residual block with a hybrid loss function for liver and tumor segmentation. A cascaded classifier framework [31] based on deep convolutional neural networks formulates segmentation as a classification problem. The study in [32] investigated the impact of various MRI images on hepatocellular carcinoma segmentation using data augmentation, a random sampling technique, and a deep fusion network. Tumor Attention Networks (TA-Nets) [33] were introduced for liver tumor segmentation, integrating multi-functional modules and tumor attention layers. Finally, Residual Multi-Scale Attention UNet (RMAU-Net) [34] incorporates Res-SE-Block and a Multi-scale Attention Block for precise liver and tumor segmentation. In [35], Residual-Atrous UNet (RA-Net) was proposed for liver tumor segmentation, incorporating an Atrous Convolution Block to address tumor heterogeneity and the Mish Activation Function for improved learning. The framework in [36] uses UNet, Atrous Spatial Pyramid Pooling (ASPP), and SE block techniques for liver CT segmentation. [PERSON] et al. [37] proposed RA-UNet, a hybrid deep attention-aware network for 3D liver and tumor segmentation from CT scans. RA-UNet improves decoder performance by utilizing a deep residual attention mechanism within a 3D UNet model, though it is computationally demanding and memory-intensive. [PERSON] and [PERSON] [38] presented MS-UNet, a multi-scale UNet with feature recalibration for 2D liver and tumor segmentation. Incorporating Res2 Net and SENet modules, this method captures multi-scale features effectively but at a high computational cost. [PERSON] et al. [39] developed DAR-net, a Dynamic Adaptive Residual Network for 2D liver segmentation from CT images. The network enhances performance using interpolation-based pooling, skip connections, and conditional random fields. However, the adaptive mechanisms add complexity and require extensive tuning.