# Circle-U-Net: An Efficient Architecture for Semantic Segmentation
## 3 Method  
### Circle-U-Net  
There is ten-block which starts with a 7 \(\times\) 7 block followed by a pooling layer called C1, then followed by C2 with three bottleneck block, C3 with four bottleneck block with one circle connect, C4 with 23 bottleneck blocks with two circles connects, and C5 with three bottleneck blocks and one circle connect. C1 to C5 forms the contracting layers that capture the context of the image, and C6 to C10 forms the expanding layers to achieve pixel-wise localization and finally forming a segmented image. C6 comprises a merging layer from C4, C7 comprises a merging layer from C3, C8 comprises a merging layer from C2, C9 gets from C1. We have C10 forming the final expanding layer, which produces the output by pixel-wise classification. Figure 1 denotes the Circle-U-Net architecture as described above. In Figure 2, the Concat-Conv Structure suggests that an up-convolution layer is merged with a layer from the contracting path, which is done to add high-resolution features from the contracting path to the expanding path for better localization, and convolution blocks are added successfully to learn to produce precise output. This is the schematic of our framework, which is described in Figure 3. An input image of 256 \(\times\) 256 size is pre-processed by resizing, normalization, and then parsing labels. It is then fed to CircleUnet for training, where the contracting path extracts the features and expands the way it localizes. In image segmentation, pixel-wise classification takes place, and we use the CCE loss function. After training with the trained model, we can predict the output. Figure 1: Circle-U-Net structure. Figure 4: Circle-U-Net with attention structure. Figure 5: Bottleneck Block Structure. \begin{table}
\begin{tabular}{c c c} \hline \hline
\begin{tabular}{c} Bottleneck with residual at first the layer \\ and circle connect at last \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The Structure of every block. Circle Connect. Circle connect is the new concept we propose through this paper. We take \(\text{C}_{x-1}\) first layer (i.e., a residual bottleneck block) and connect with the end of Layer x as described in Figure 6. By Table 1 we have proved that using such types of connecting layers for every periodic layer has increased accuracy. When a layer has the information of its far previous it can associate and learn current features with the farther ones. We have four circles connect layers in our architecture, as described in Table 2.  
Gated signals. The gated signals include resizing the layer feature map to the up-layer feature map, using \(1\times 1\) convolution. The gating signal used with skip connection aggregates image features from multiple image scales and is fed for attention blocks. Attention gates. Attention gates (AG) is proposed by [PERSON] et al. (2016). AG could learn to automatically focus on the sizes and shapes of target structures. AGs are used when the model implicitly learns to suppress irrelevant features and highlights salient features. When AG is used for CNN models such as U-Net, it could increase prediction accuracy and sensitivity. We are using attention gates in the expansion path at C6, C7, C8, and C9 layers in Circle-U-Net with Gated Signal Attention (GSA) network. AG self-learns to focus on the target structure of different shapes and sizes. Where AGs are used, the model implicitly learns to suppress irrelevant features and highlights salient features. Figure 7 describes attention gates, where gating signal and layer x are concatenated and passed on with Relu activation, which when further resampled, produces attention layer.