# Predicting Multi-Gene Mutation Based on Lung Cancer CT Images and Mut-SeResNet
## 2 Related Work  
Deep learning is a process that simulates the cognition of human things and learns them. Deep learning techniques take data through nonlinear transformations [12, 13] toperform high-dimensional abstractions. They extract and combine various types of shallow features to transform them into more representative higher-level features, which describe the intrinsic properties of things.  
In recent years, deep learning [14; 15; 16; 17] has been widely used in the medical field. The convolutional neural network (CNN) solves the tedious engineering of traditional manual feature extraction by mimicking the mechanism of biological visual perception to achieve automatic feature extraction from data [18].  
[PERSON] et al. [19] designed a MCNN (multi-scale convolutional neural network). The input images were processed from three scales. They were passed as inputs to three convolutional networks with shared weights to overcome the classification challenges brought on by the variable size of lung nodules. [PERSON] et al. [20] proposed to train 3D CNN architecture for non-specialized pathology images. The images were extended to a specialized pathology image dataset using transfer learning to overcome the difficulty of accessing a CT pathology lung nodules image dataset; [PERSON] et al. [21] proposed deep learning architecture that used the combination of the dual path network (DPN) [22] and the gradient boosting machine (GBM). In 2017, [PERSON] [23] et al. proposed using a deep residual network to solve the problem of benign and malignant classification of CT images. Previous studies focused on the benign and malignant classification of lung nodules, while our paper focuses on predicting the mutation or lack thereof of genes by CT image information. In the past, the medical approach was used to predicted gene mutation, while we used deep learning methods.  
Studies have shown that mutations in the epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma (KRAS) are the most common driver genes in NSCLC [24; 25; 26; 27]. Biopsies are usually performed using endoscopic or fine-needle aspiration methods to obtain specimens for EGFR and KRAS mutation testing. However, some patients cannot undergo invasive mutation testing at high frequencies [28; 29]. Exploring a non-invasive way to detect mutations has some clinical value. However, gene mutation diagnosis relies on a large amount of imaging datasets. The morphological features of lung nodules show complex and irregular shapes. There is no clear cut-off for whether a gene is mutated or not. This puts high demands on the deep convolutional networks' extraction of critical graphical information embedded in lung CT images.  
Based on the above problems, this paper proposed a genetic mutation model based on the ResNet [30] network that incorporated the residual block [31] and attention mechanism, or a Mut-SeResNet model. The residual structure was introduced to solve the performance degradation problem caused by the deepening of network depth. When the network cannot learn new features, the model can realize identity mapping through the residual structure, extract the slight differences between different levels, and enhance the feature learning ability. The Mut-SeResNet model introduced the squeeze and excitation attention mechanism [32] to portray the interdependence between channels of the CNN. The net is expected to calibrate the self-adaptive strength of channel feature and improve the performance of deep convolutional networks. Finally, the constructed model combined multi-task learning to predict EGFR and KRAS mutations [33]. Our proposed Mut-SeResNet model could effectively determine whether a patient has lung cancer or not based on the predicted gene mutation, and it may provide an aid for doctors to make a judgment.