# Predicting Multi-Gene Mutation Based on Lung Cancer CT Images and Mut-SeResNet
## 3 Materials and Methods  
In this paper, we designed a model called Mut-SeResNet, which consisted of four parts that were differentiated according to the depth of nodule feature extraction. The framework of our proposed Mut-SeReSnet model was shown in Figure 1. Stage1 mainly performed the convolutional processing of images to extract nodule features initially. Stage2 used a 1 \(\times\) 1 convolutional layer to connect residual and attention blocks to achieve cross-channel interaction and to integrate lung nodule information from previous modules. Stage3 consisted of a residual block and an attention block repeated three times to extract the fine features of lung nodules. Stage4 performed a multi-task prediction of whether EGFR and The model used the ResNet-18 network as the base network by using four modules consisting of residual blocks. The block had four convolutional layers. Finally, a fully connected layer was passed. The Mut-SeResnet adds residual blocks without increasing the parameters, and it is therefore suitable for extracting the depth features of nodules. Stagel was the input layer, which was responsible for processing and loading images. To alleviate the overfitting problem of the model caused by insufficient data, the data augmentation technique was used to expand the training dataset. Each slice generated four enhanced images by performing an image rotation, a vertical flip, a horizontal flip, and by changing the image brightness for each 2D nodule slice. The input images were processed from EGFR and KRAS datasets. Then, all images were uniformly scaled to 224 \(\times\) 224 to accommodate subsequent model processing. Immediately after the data augmentation, the input images were down sampled through a series of convolutional layers. These layers were a \(7\times 7\) convolutional layer, a batch normalization layer, and a \(3\times 3\) maximum pooling layer. They were used to retain as much original information about the images as possible. Stage2 adjusted channels and resolution by introducing an additional \(1\times 1\) convolution layer. The layer changed the number of channels so that the input and output could be consistently added. The nonlinear expression capability and the network's performance were improved while keeping the feature map's scale unchanged. Since Stage 1 already used a maximum pooling layer with a step size of 2, the desired feature extraction effect could be obtained without reducing the channel width. The squeeze and excitation  
Figure 1: The framework of our proposed Mut-SeResnet model. network attention mechanism was used after the residual module (specifically described in Stage3). CT images are natural 3D image sequences. Each section often has complex context and semantics. The attention mechanism uses the global field of perception to dynamically adjust the importance of each channel in the feature map. The mechanism mines the correlation information between different dimensions of the feature map to assist in the extraction of CT image features of lung nodules, thus improving the accuracy of distinguishing malignant nodules. In Stage3, the residual block was passed through two identical output channels of 3 \(\times\) 3 convolutional layers. A batch normalization layer and the ReLU function followed the convolutional layers. Then, the input was added directly before the final ReLU activation function through the cross-layer data path by skipping two convolutional operations. In contrast to Stage2, Stage3 did not include a 1 \(\times\) 1 convolution layer. After the first residual block, each subsequent module halved the height and width of the previous module to expand the perceptual field. The block doubled the number of channels to make the information between different channels of the graph able to be fused. The structure of Stage2 and Stage3 was shown in Figure 2.  
Let the input in a network model be x. The desired output of the network is H(x). The residual mapping is F(x).