# Construction of Apple Leaf Diseases Identification Networks Based on Xception Fused by SE Module
## 3 Disease Classification Models of Xception Fused by the SE Module  
### Deep Fusion of the SE Module and Depth-Wise Separable Convolution  
Xception [35] uses depth-wise separable convolution instead of traditional convolution operation. This helps maintain the classification accuracy while reducing the number of parameters and the amount of computation. The depth-wise separable convolutions used in Xception performs spatial convolution independently in each channel, followed by point-wise convolution. This facilitates the possibility of improving the model from the channel dimension.  
Using channel dependency (namely channel correlation) is an important way to improve CNNs [25]. SE module adopts the Squeeze-Excitation-Reweight method. First, the information of all channels is summarized and compressed, then the weight of channels is calculated through the two fully connected layers and activation function. Finally, the weight is multiplied with the original feature by channel-wise, so as to complete the adaptive feature recalibration, namely channel weighting. [PERSON] et al. [25] analyzed the computational complexity of SE module in their paper, and the experiment proved that the number of parameter of SE-ResNet-50 increased by about 10% compared with ResNet-50, while the calculation cost would increase by less than 1%, theoretically. Therefore, it is expected that the fusion of SE module into Xception will not bring obvious changes in the number of parameters and calculation time.  
There are two problems to consider when introducing Squeeze-and-Excitation into Xception. Which convolution layers of the network to insert SE modules? How to fuse the SE module into Xception? The author of SENet found that the addition of the SE module into deep layers of CNNs had limited improvement of accuracy on the classification tasks. Therefore, to improve the classification performance without introducing too many extra parameters, we would not add SE modules into the deep layers, but add it to the shallow and middle layers of Xception. The author of SENet proposed four schemes to insert SE modules as independent modules into the CNN models, as illustrated in Figure 3a-d, but these four schemes did not directly weight the feature channels from the low level. Based on depth-wise separable convolution, this paper proposes to insert the SE module between the depth-wise convolution and the point-wise convolution of the depth-wise separable convolution, as shown in Figure 3e. Therefore, the SE module can perform feature recalibration directly on the feature channel from the lower level, which makes the model more sensitive to the important features for the current classification task.  
Figure 2: The data augmentation examples of training set: (**a**) Original image; (**b**) Vertical mirroring; (**c**) Horizontal mirroring; (**d**) Rotating 90\({}^{\circ}\); (**e**) Rotating 180\({}^{\circ}\); (**f**) Rotating 270\({}^{\circ}\); (**g**) Weak sharpening; (**h**) Strong sharpening; (**i**) Weak light; (**j**) Strong light; (**k**) Weak contrast; (**l**) Strong contrast; (**m**) Gaussian noise; (**n**) PCA jitter.