## 3 Automated Diagnosis of COVID-19
### Image-Level Diagnosis: Classification-Based Models
#### 3.1.4 Classification  
Classification is used to present a diagnosing prediction (such as COVID-19/non-COVID-19) according to the extracted feature. Most existing COVID-19 diagnosing models use CNN as the feature extractor, and most of them use softmax as the classifier. Some researchers proposed improvements based on the CNN-softmax scheme. For example, [PERSON] et al. [1] combined softmax, decision tree and Adaboost algorithms, and [PERSON] et al. [113] simultaneously performed softmax loss-based classification and contrastive loss-based anomaly detection to make the final decision. However, these deep models are black-box and usually need large-scale training sets. In the literature of [45; 109; 164], researchers developed non-end-to-end models and took the Support Vector Machine (SVM) as the classifier. Comparative experiments of classification algorithms, including SVM, logistic regression, k-Nearest Neighbors (k-NN), Multi-Layer Perception (MLP), decision tree, AdaBoost, random forest, LightGBM [165], and bagging classifier have been carried out in [119; 156; 162; 163]. Among them, classifiers in [119; 156] are for visual feature classification, whereas classifiers in [162; 163] are for hand-crafted clinical feature classification, in which, Least Absolute Shrinkage and Selection Operator (LASSO) [166] and deep forest [167] algorithms were used for feature selection. A straightforward way of modeling the COVID-19 diagnosing task into a classification task is through binary classifying the scanning images into a COVID-19 class and normal class; this is adopted by many models [40; 41; 45; 47; 49; 50; 53; 54; 58; 62; 65; 67; 83; 110; 111; 113; 119; 126; 138; 143; 148; 150; 152; 164; 168; 169; 170; 171; 172; 173]. However, in practice, test images of other types of abnormal lungs can be misclassified as COVID-19. As shown in Figure 6, diagnosing COVID-19 is a fine-grained task. Lung diseases that belong to the same subclass share similar patterns in scanning images, and have a chance to be misclassified. Researchers overcome the problem of misclassification mainly through two approaches: multi-class classification and multi-step classification. For multi-class classification, some researchers added other pneumonia categories in addition to the binary classification tasks, such as viral pneumonia, bacterial pneumonia, Community-Acquired Pneumonia (CAP) and non-COVID-19 pneumonia. As summarized in Table 5, the classification tasks of COVID-19/normal/viral pneumonia/bacterial pneumonia [44; 48; 52; 74; 93; 95; 114; 116; 118; 122; 124; 133; 155; 176; 177; 178; 179; 180; 170; 171; 172; 174; 175; 176] and COVID-19/non-COVID-19 pneumonia [41; 177; 178; 179; 180; 181; 182; 183; 184; 185; 186; 187; 188; 189; 181; 183; 185; 187; 189; 188; 184] are the most popular setting. Some models also take into account other types of lung diseases, including ARDS [109; 46; 109], tuberculosis [95; 96; 185], lung cancer [42; 161], pneumocystis, streptococcus, varicella [156], fevers and upper respiratory tract symptoms [117] and Interstitial Lung Disease (ILD) caused by other viruses [61]. Figure 6: An example of the hierarchical relationships of different lung diseases. However, methods of multi-class classification rely heavily on datasets. Meanwhile, the model cannot learn the hierarchical relationships between categories. Multi-step hierarchical classification is used to help the models learn such hierarchical relationships. For example, [PERSON] et al. [75] and [PERSON] et al. [140] trained two binary classifiers: one for normal/pneumonia classification and one for further COVID-19/non-COVID-19 classification. [PERSON] et al. [98] firstly classified the screening image into normal/bacterial pneumonia/viral pneumonia, and then performed the COVID-19/non-COVID-19 classification. [PERSON] et al.