## 3 Automated Diagnosis of COVID-19
### Image-Level Diagnosis: Classification-Based Models
#### 3.1.3 Feature Extraction  
As previously demonstrated in Section 2 and shown in Figure 2, scanning images of COVID-19 has certain characteristic manifestations, such as Ground-Glass Opacity (GGO) and a crazy-paving pattern distributed in a certain zone of lungs [16]. Feature extraction detects those discriminative lesion patterns. Most COVID-19 diagnosing models adopted the Convolutional Neural Network (CNN) for feature extraction, and most of them used existing network structures, such as ResNet [37], GoogLeNet [100], DenseNet [101], VGG [102], MobileNet [103], SqueezeNet [104], AlexNet [105], Capsule [106], etc. We summarized some popular CNN structures that have been used by COVID-19 diagnosing models in Table 4.  
Figure 5: Significant variation among CT images caused by different scanners. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Paper** & **Rotating or Flipping** & **Scaling or Cropping** & **Brightness Adjusting** & **Contrast Adjusting** \\ \hline
[47; 49; 52; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80] & \(\surd\) & - & - \\
[45; 51; 81] & \(\surd\) & \(\surd\) & \(\surd\  
Some researchers also proposed automatic network structure designing methods to identify the best network structure for lung feature extraction. [PERSON] et al. [155] used a generative synthesis approach to identify the optimal network architecture. [PERSON] et al. [46] applied a Gravitational Search Algorithm (GSA) to determine the best network architecture hyperparameters. [PERSON] et al. [137] developed an iteratively pruning strategy to identify the optimal network structure. The model ensemble can also promote the overall performance. [PERSON] et al. [118] and [PERSON] et al. [45] performed a model ensemble by voting and feature fusion. [PERSON] et al. [95] applied Softmax Class Posterior Averaging (SCPA) and Prediction Maximization (PM) for the model ensemble, and [PERSON] et al. [156] combined seven traditional feature extraction models with Inception-v3 to obtain better results. [PERSON] et al. [145] assembled different CNNs using a stacked generalization approach [115] to further improve the model performance. These models assumed that different sub-models learn nonlinear discriminative features and semantic image representation from images of different levels. Therefore, the combined model will be more robust and accurate. In the beginning, trying existing CNNs is fast and convenient. However, these networks are designed for general image classification tasks, such as the ImageNet challenge. Radiologists diagnose COVID-19 by finding distinguishing local patterns. Some researchers design local methods to extract more discriminative features. For example, [PERSON] et al. [45] and [PERSON] et al. [96] used the local patches to train the CNN feature extractor. To analyze local textural features, [PERSON] et al. [157] proposed the Grey Level Co-occurrence Matrix (GLCM). However, lung infectious areas may vary significantly in size, and the local methods with a fixed patch size are unable to extract features of the target with the larger size. [PERSON] et al. [158] proposed multi-scale learning to overcome such deficiencies. The network aggregated features from different layers to make the final decision. Similarly, [PERSON] et al. [108] and [PERSON] et al. [108] integrated ResNet or DenseNet with the Feature Pyramid Network (FPN) [159], which is a pyramidal hierarchy network structure for multi-scale feature extraction. In addition, the lesion of COVID-19 in the lung is a 3D object, and slice-wise contextual information in CT images would be lost by a conventional 2D feature extractor. Therefore, [PERSON] et al. [41], [PERSON] et al. [151], [PERSON] et al. [61] and [PERSON] et al. [78] proposed CNN structures with 3D convolution units to detect COVID-19 to solve this defect. [PERSON] et al. [160] proposed an Attention-based Deep 3D Multiple Instance Learning (AD3D-MIL) algorithm that can predict the infection according to multiple CT slices.