# Advances in Computer-Aided Medical Image Processing
## 4 Neural Network Design for Medical Imaging
### The Dimension of Medical Image Feature Extraction  
Inspired by human neuroscience [83], in the CNN hierarchical representation image, the bottom feature is a local operator related to direction, the middle feature is a local feature with semantics, and the high-level feature is a semantic feature. Convolutional  
Figure 8: The three-dimensional convolution effect. The blue picture is the original multidimensional feature map, the purple is the three-dimensional convolution operator, the green is the output image feature, and the number of channels of the operator is hidden. Figure 9: The features of medical images in different dimensions. The left side is the T1-weighted image, the middle is the T2-weighted image, and the right side is the FLAIR-weighted image. It can be seen that images in various dimensions provide different features. neural networks extract important features by filtering input functions and cross-correlation functions, such as Formula (2). \[\mathrm{S}\big{(}\mathrm{w},\;\mathrm{h},\;\mathrm{d}^{\prime}\big{)}=(\mathrm{I} \bigstar\mathrm{K})(\mathrm{w},\mathrm{h},\mathrm{d}^{\prime})=\sum_{\mathrm{k} =1}^{\mathrm{c}}\sum_{\mathrm{i}=1}^{\mathrm{k}}\sum_{\mathrm{j}=1}^{\mathrm{ k}}\mathrm{I}(\mathrm{w}+\mathrm{i},\mathrm{h}+\mathrm{j},\mathrm{k})\mathrm{K}( \mathrm{i},\mathrm{j},\mathrm{k}) \tag{2}\]  
In Equation (2), \(\mathrm{w}\) stands for medical image width, \(\mathrm{h}\) stands for medical image height, \(\mathrm{d}\) stands for medical image dimension, \(\mathrm{I}\) stands for original image, and \(\mathrm{K}\) stands for convolution kernel. \(\bigstar\) stands for cross-correlation function. Medical image features are hierarchical. The more layers there are, the more channels in each layer there are, and the richer are the extracted features. To improve the segmentation accuracy, the depth learning model must fully extract the rich features contained in each mode of multimodal medical images as much as possible. At the same time, it is also necessary to consider reducing parameters in practical operation to improve the training and reasoning speed. The expression ability of a neural network is similar to an exponential function. The width of the network determines the base number of the function, and the depth of the network determines the exponent of the function. Compared with the ReLU operation proposed by the traditional LeNet [84], AlexNet [85] can better back propagate and increase the learnability of the overall network compared with the previous tanh operation. At the same time, the AlexNet network uses Dropout0.5 [86], L2 weight decal [87; 88; 89], and data enhancement operations to considerably reduce the parameters of the MLP layer, thereby reducing overfitting. [PERSON] proposed the VGG network [90]. He proposed replacing the large convolution kernel with a smaller convolution kernel to obtain a deeper number of layers. As shown in Figure 10, he found that the receptive field of two 3 \(\times\) 3 convolution cores is the same as that of one 5 \(\times\) 5 convolution core. This increased the number of layers of the network, and activation functions can be added between layers, increasing the nonlinear expression ability of the network. At the same time, he used fewer parameters, but because the computing power at that time was not enough to train such a deep network, VGG introduced pretraining. The basic module of GoogLeNet, Inception, is the first to incorporate the idea of feature augmentation [91] into the design of network structures. Each convolution core of different sizes extracts different features and connects different features after they become the same dimension through padding. The Inception structure uses a 1 \(\times\) 1 convolution to raise and lower dimensions and clusters the sparse matrix into a relatively dense submatrix to improve computing performance. At the same time, multiple dimensions can be used for convolution and reaggregation at the same time.