# Advances in Computer-Aided Medical Image Processing
## 4 Neural Network Design for Medical Imaging
### Reuse of Low-Level Features in Medical Images
#### 4.1.1 U-Shaped Structure Reuse Low-Dimensional Edge Features  
The internal structure of the human body is relatively fixed, the distribution of segmentation objects in the human body image is very regular, the semantics are simple and clear, and low-resolution information can provide this information for target object recognition [76]. Therefore, CNN can still be used for feature extraction of such tasks. However, due to the fuzzy boundary and complex gradient of medical images, more high-resolution information is required for accurate segmentation, which is obviously inconsistent with the feature extraction part. The feature extraction loses the original low-dimensional high-resolution features in exchange for multidimensional low-resolution \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Tumor Types** & **Data Complement** & **Method** & **Result** & \\ \hline \multirow{6}{*}{Brain tumor} & 165 child subjects & Learning method based on local shape and & \multirow{2}{*}{DICE} & 0.779 \\  & & sparse appearance [62] & & \\  & Kaggle & Multilayer perceptron algorithm in & & ACCURACY & 86.63\% \\  & BRATS 2017 & VGG-19 with hand-marked features [22] & DSC & 0.99 \\  & Figshare & DenseNet21 and Adadelta with transfer & \multirow{2}{*}{ACCURACY} & 98.91\% \\  & BRATS 2018 & U-Net with Squeeze-and-Excitation [23] & & DICE & 0.823 \\  & HECKTOR2020 & U-Net with SA [73] & & ACCURACY & 75.2 \\  & CT images of Naval & \multirow{2}{*}{Fuse a 2D CNN with a 3D CNN} & \multirow{2}{*}{ACCURACY} & 87.60\% \\  & General Hospital & & & \\ ABIDE & U-Net with VINN [26] & DSC & 1.46 \\  & HECKTOR 2020 & 3D FCN uses two independent V-Net [45] & & ACCURACY & 89.10\% \\ \hline \multirow{6}{*}{Liver tumor} & ImageCLEF & Entropy-based fuzzy region growth & \multirow{2}{*}{DSM} & 0.88 \\  & MIDAS & & Fuzzy C-means [53] & DSC & 0.81 \\  & MMWR2017 challenge & UDA architecture with transfer learning [31] & ACCURACY & 78.50\% \\  & Benchmark dataset & \multirow{2}{*}{GoogLeNet with transfer learning [37]} & ACCURACY & 97.53\% \\  & LiTS & V-Net [74] & & DICE & 0.954 \\  & SLiver07 & 3D CNN to directly guide the objective & \multirow{2}{*}{VOE} & 5.42 \\  & 3 DIRCADb & & H-DenseUNet [39] & & ACCURACY & 93.70\% \\  & MRI from McGil Health & \multirow{2}{*}{U-Net extracted multiscale features of [75]} & \multirow{2}{*}{ACCURACY} & 81.98\% \\  & Center & & different modes [75] & & ACCURACY & 81.98\% \\ \hline \multirow{6}{*}{Breast Tumor} & TUPAC 2016 & CNN modelling method based on & \multirow{2}{*}{ACCURACY} & \multirow{2}{*}{82\%} \\  & BreakHis & & explainable context [36] & & \\ \cline{1-1}  & BreakHis & ResNet with Inception model [11] & & ACCURACY & 82.60\% \\ \cline{1-1}  & BreakHis & ResNet50 with transfer learning [64] & & ACCURACY & 98\% \\ \hline \multirow{6}{*}{lung tumor} & 107 different patients & Fractional Browne motion model [67] & ACCURACY & 88.82\% \\ \cline{1-1}  & 952 patients & YOLO v3 with Google Inception & \multirow{2}{*}{ACCURACY-} & 95.52\% \\ \cline{1-1}  & & v3 network [68] & IMPROVE & \\ \cline{1-1}  & COVID-19 & \multirow{2}{*}{AlexNet with transfer learning [46]} & \multirow{2}{*}{ACCURACY} & 99.05\% \\ \cline{1-1}  & STS & U-Net with multimode spatial & & \\ \cline{1-1}  & & attention module [42] & & ACCURACY & 71.44\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary and comparison of different medical imaging methods. features. Faced with the problem of sampling, Figure 6 shows the U-shaped network structure; this architecture, noted for its \"U\" shape, is designed with a unique two-part structure composed of a contracting path (encoder) and an expansive path (decoder). A standard U-Net has 23 convolutional layers, divided between these paths. The encoder is a series of convolutional layers and max pooling layers, which help the network to learn the contextual/spatial information about the input images.