## 2 Materials and Methods
### The Proposed Method
#### 2.2.2 The Improved U-Net++ Network  
U-Net++ has the outstanding improvement in feature transmission and combination of deep and shallow layers, but there is no improvement in feature extraction. There are some diverse edema regions whose features may lose. In order to extract edema features better and improve segmentation effects, this paper conducted a two-stage optimization improvement on the U-net++ and the backbone network part. First of all, due to the residual block design of the Residual Network (ResNet), the network will not degrade with the deepening of the layer, and thus it is more suitable as a backbone network than other networks. Then, the selected ResNet backbone network was further improved in the two aspects of information flow through the network and shortcut, so that the segmentation accuracy of the model in the diverse edema region was improved. There are many networks that can be selected as the backbone, such as VGGNet, ResNet, DetNet, and DenseNet. Among them, ResNet is a powerful convolutional neural network (CNN)  
Figure 4: Structure diagrams with both the first layer \(L1\) and the second layer \(L2\): (**a**) \(L1\); (**b**) \(L2\). architecture [26]. Its main structure is several ResBlocks (residual block). The specific design of the ResBlock can solve the problem of network degradation caused by the deepening of the network, making thousands of layers of networks possible, and has been widely used in various tasks. In this paper, after optimization and comparison, the ResNet was used as the backbone of U-Net++ to achieve the best results. Table 1 lists the ResNet structure of 34 layers and 101 layers. It is shown that the network structure can be divided into two parts, five main Conv layers (including ResBlocks) and an end phase. In the five main phases, each Conv layer contain several blocks except Conv1. The ResNet34 network's Conv2 layer has three ResBlocks, Conv3 has four ResBlocks, Conv4 has six ResBlocks, and Conv5 has three ResBlocks. As shown in Figure 5, this proposed method uses ResNet's Conv1, Conv2, Conv3, Conv4, and Conv5 as U-Net++'s backbone, instead of \(X^{(0,0)}\), \(X^{(1,0)}\), \(X^{(2,0)}\), \(X^{(3,0)}\), and \(X^{(4,0)}\), and changes all \(3\times 3\times n\) to \(7\times 3\times 64\) convolutional blocks that are used for feature extraction, ensuring the receptive field of the last encoder block includes the entire retinal region. The \(1\times 1\times n\) convolution block becomes \(1\times 1\times 64\). The improved network will significantly improve the segmentation effect of macular edema, compared with the previous U-Net++. The second step of improvement is to learn from ResNeSt [27], which is the latest network proposed in April 2020, which makes ResNet trainable network reach more than 3000 layers and has improved accuracy. The ResNeSt network has made the following improvements [27]. (1) Improved information flow through the network. As shown in Figure 5(a), the main structure of ResNet is ResBlock block. Each ResBlock contains two \(1\times 1\) and one \(3\times 3\) convolutions, and then three ReLU layers are added. The large gray arrow represents the direct connection of information; there is also a ReLU activation layer on the main channel of the gray arrow. The ReLU at this position will clear the negative weight, especially at the beginning of training, because there will be a large amount of negative weight, and thus the ReLU will have a negative impact on information transmission. To solve this problem, the pre-activation [28] is proposed by an improved method, as shown in Figure 5(b). By changing the positions of BN and ReLU, the ReLU is removed in the straight connected path, and placed in the ResBlock. However, this method increases the learning difficulty, the lack of non-linearity between blocks, and limits the learning ability.