## 3 SEM-RCNN-Based EM Detection Method
### Feature Extraction Step
#### 3.1.4 Backbone of Feature Extraction Step  
By fusing ResNet, SENet, and FPN, we finally design the backbone of SEM-RCNN, as shown in Figure 6. There are mainly five steps in the red dashed box in Figure 6. In stage one, 64 convolution kernels with a kernel size of \(7\times 7\) and a stride of 2, followed by batch normalization (BN) and ReLU operations, were applied for feature extraction. Then, max-pooling with a size of \(3\times 3\) and stride of 2 was applied to reduce the size of feature maps. After that, the following four steps are based on the combination approach of SER block_1 and SER block_2. The SER block_1 and SER block_2 are important parts of the backbone, and its structure are shown in Figure 7. From Figure 7, we can see that the SER block_1 is mainly used to deal with the case where the input and output dimensions are different; the SER block_2 is mainly used to deal with the case where input and output dimensions are the same. For the input parameters of SER block_1, the channel of the input image is denoted as C, the width (same as height) of the input image is denoted as W, the output channel of the feature map is denoted as C1, and the stride is denoted as s. For example, in stage two of Figure 6, input images are processed at the size of 54 \(\times\) 54 with 64 channels. Hence, in the first step of SER block_1, 64 convolution kernels with the kernel size of \(1\times 1\) and stride of 1 (followed by BN and ReLU operations) were applied first. Then, the convolution kernels with the kernel size of \(3\times 3\) and \(1\times 1\) were applied sequentially to extract the feature and to adjust channels. At the same time, the input image is processed  
Figure 5: The structure of FPN.  
by 64 \(\times\) 4 convolution kernels with the kernel size of 1 \(\times\) 1 and stride of 1 (right part of SER block_1). Finally, the feature maps after global pooling and sigmoid (the left part of SER block_1) are residually connected with the right part of SER block_1. The output size of SER block_1 is 56 \(\times\) 56 with channels of 64 \(\times\) 4. The structure of SER block_2 is similar to SER block_1, but the output size is the same as the input size. The combination method of SER block_1 and SER block_2 repeats four times in SEM-RCNN for feature extraction.  
Figure 6: The backbone structure of SEM-RCNN. Red dashed box is SENet and ResNet part; blue dashed box is FPN part.  
Figure 7: The structure of SER block_1 and SER block_2.  
Based on our sufficient research foundation [15; 20; 21; 22; 24; 29], it can be found that only a few studies employed deep learning methods to perform the detection task in microorganism image analysis. Since the detection task in microorganism image analysis has strong application background, almost all studies directly utilize existing deep learning models, such as RCNN [88] and Faster R-CNN [84; 85]. Different from these studies, we proposed a novel self-attention-based two-stage detection framework, which is inspired by ResNet, SENet, and FPN. This framework achieves state-of-the-art performances on the detection task, which significantly promotes the development of the detection technology in the application of microorganism image analyses.