# Deep Learning Based Semantic Image Segmentation Methods for Classification of Web Page Imagery
## 2 Related Works  
Fully convolutional networks (FCNs) may be used for image segmentation and localization; however, they need a large amount of training data. Due to the time-consuming nature of manually labeling images with various categories, few datasets with pixel-level labels are available to the public. When segmenting images, FCNs [31; 32; 33] and UNets [34; 35; 36; 37; 38] are often used to address these difficulties. [PERSON] et al. [31] developed a novel image segmentation network (R-Net). Even with mixed noises present in the input photos, the suggested R-Net model still performed well on low-resolution images. Three modules make up the planned R-Net model: a module for data augmentation, a module for developing the CNN architecture, and a module for building the loss function. Our R-Net model can extract the discriminative feature for clean edge segmentation in comparison to the current networks. [PERSON] et al. [32] introduced an attention mechanism at the end of the FCN that can extract image depth features and increase segmentation accuracy. The proposed model is based on an improved fully convolutional network that also combines data pre-processing and extracts texture information of the biomedical images acquired by biomedical sensors using Gabor filters to deepen image texture. The stability and segmentation quality of the segmentation network are further enhanced by pre-processing the input and including the attention mechanism in the FCN. Studies have revealed that combining the attention mechanism with a fully convolutional network improves segmentation accuracy and stability and brings segmentation closer to artificial segmentation. [PERSON] et al. [33] suggested using the UNet model, which has the best performance in the real-world forest fire segmentation scenario, with a better description of semantic details; however, the inference speed for a single instance is relatively slow, making it more suitable for application scenarios requiring high accuracy. [PERSON] et al. [34] suggested a UNet model based on several attentions (MA-UNet) with a residual encoder based on a straightforward attention module to enhance the backbone's capacity to extract fine-grained features. The semantic representation of the feature map is rebuilt using multi-head self-attention for the lowest level feature while providing fine-grained segmentation for various pixel categories. Then, to address the issue of multiple scales in various categories, they add downsampling to subdivide the target's feature sizes at various scales and use channel attention and spatial attention in various feature fusion stages to better fuse the target's feature information at various scales. [PERSON] et al. [36] proposed using pre-trained CNN segmentation techniques such as SegNet, UNet, and VGG-UNet to more accurately separate leukocytes. According to the experimental findings, VGG-UNet outperformed SegNet and UNet in terms of performance. The VGG-result UNets are also validated against various hybrid segmentation techniques that have been documented in the literature. [PERSON] et al. [37] suggested a tumor segmentation system based on categorization, edge detection, and picture enhancement using fuzzy logic. Dual tree-complex wavelet transform (DTCWT) is utilized at various scale levels to identify the edge in the source pictures after the input images have been pre-processed using contrast enhancement and fuzzy-logic-based edge identification. The features are determined from degraded sub-band pictures, and they are further classified using a UNet CNN. [PERSON] et al. [38] suggested a quantization-assisted UNet technique for segmenting breast lesions. UNet and quantization are the first and second segmentation steps. When using UNet-based segmentation to separate precise lesion regions from sonography pictures, quantization is a helpful tool. The separated lesions are then employed in the independent component analysis (ICA) approach to extract features, which are subsequently merged with deep automated features.