## 3 Automated Diagnosis of COVID-19
### Image-Level Diagnosis: Classification-Based Models
#### 3.1.2 Preprocessing  
In the existing literature, researchers mainly used three types of preprocessing methods: data augmentation, image equalization and lung segmentation. Data augmentation can enlarge the dataset and prevent overfitting, equalization improves the image quality and lung segmentation can preserve the region of interest (ROI) only and avoid the undesired interference from areas out of the lung.  
To avoid overfitting and solve the problem of data imbalance, data augmentations are the most adopted method in the preprocessing stage. Rotating, flipping, scaling, cropping and brightness and contrast adjusting [40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54] are the simplest and most common data augmentation methods. For simplicity, in Table 3, we summarize basic transformation-based data augmentation methods used by COVID-19 diagnosing models. We also summarize the total number of papers that adopt each type of data augmentation in the last row of Table 3. It can be seen that rotating and filling and scaling or cropping are the most widely adopted techniques. However, their augmentation strength is limited. For example, in the comparative experiment of [PERSON] et al. [55], the conventional data augmentation method improved the diagnosing performance by only 4%. Therefore, researchers proposed applying other advanced data augmentation methods. To balance the imbalanced data, [PERSON] et al. [56] proposed the synthetic minority oversampling technique (SMOTE). [PERSON] et al. [52] performed Zero-phase Component Analysis (ZCA whitening) to remove redundant information in input scanning images. [PERSON] et al. [57] and [PERSON] et al. [58] used the Generative Adversarial Network (GAN [59] and Conditional Generative Adversarial Network (CGAN [60]), respectively, to generate virtual samples for data augmentation. Generative models can significantly increase the dataset size, but the quality of the generated sample is difficult to guarantee. The purpose of data augmentation is to prevent overfitting by increasing the variation, but, in these virtual sample  
Figure 4: A typical ResNet-classification-based image-level COVID-19 diagnosis model.  
methods, the discriminant lesion patterns might be lost or distorted if the model increases the variation too much.  
In addition to the issue of insufficient data or imbalanced data, there are also large image variations that are caused by different types of scanners. As shown in Figure 5, we can observe significant image variation across different CT scanners. To solve this issue, [PERSON] et al. [95] and [PERSON] et al. [96] performed histogram equalization on the images. However, histogram equalization has the potential harm of affecting image details or bringing unexpected noise. [PERSON] et al. [95] eliminated the noise by introducing a Perona-Malik Filter (PMF) [97], whereas some other researchers [93; 94; 98; 99] solved the problem by proposing the Contrast Limited Adaptive Histogram Equalization (CLAHE).  
Lung segmentation aims to preserve only the lung area. This is motivated by prior domain knowledge: COVID-19 is a type of viral pneumonia, and the evidence of infection cannot lie outside of the lung. Lung segmentation can be conducted by using pretrained lung segmentation models. Importantly, we note that, though segmentation models are used and segmentation masks are predicted in this step, it has a fundamental difference to the pixel-level diagnosis model. Lung segmentation is independent of identifying COVID-19. It only requires identifying the lung area. Differently, pixel-level diagnosis needs to locate the exact area of the COVID-19 lesion.