# COVID-19 Detection from Radiographs: Is Deep Learning Able to Handle the Crisis?
## 2 Related Work  
This section of the article presents the information about the datasets and the methods developed to deal with COVID. However, it should be noted that we are not interested in listing the networks that are specifically designed to detect, localize, or classify COVID cases or produce comparisons between them. Instead, we aim to review and employ traditional deep learning networks to observe their capability in handling the disease. Moreover, we provide these specifically designed methods as a literature review for our proposed algorithm and a starting point for future research. A considerable amount of research has been published on COVID-19 detection [3; 4; 5; 6], but none covered comprehensively applying different deep learning architectures. [PERSON] et al. [7] retrospectively collected 46,096 high-quality CT images from 106 admitted patients anonymously. There were 51 laboratory-confirmed COVID-19 patients, while the remaining 55 were patients of different diseases. Three expert radiologists with over 5 years of experience annotated the COVID-19 dataset with combined consensus. The problem was framed as a segmentation task, and UNet++ [8] was trained to segment the valid areas in the CT images. The trained model was deployed at the Renmin Hospital of Wuhan University and as a web API to assist in the diagnosis of COVID-19 cases worldwide. COVNet [9] uses a 3D deep learning framework to extract 2D local and 3D features for the detection of COVID-19. ResNet [10] is employed as the backbone to extract features from the input CT slices. The extracted features are passed through the max-pooling operation. The final feature map is fed through the fully connected layer and eventually through the softmax function to find the probabilities for each class. Previous studies have shown the successful application of deep learning methodologies to chest X-rays to diagnose bacterial and viral infections [11; 12]. A deep learning-based CT diagnosis system, termed DeepPneumonia, was proposed to detect and localize the lesions causing COVID-19 [13]. First, the lung region is extracted in each CT image and then fed to the Details Relation Extraction Neural Network (DRENet) to produce the top K details in a CT scan using pre-trained ResNet with a Feature Pyramid Network (FPN) and attention module. The attention module is used to learn the importance of each detail. The aggregation module aggregates the predictions to predict the patient-level diagnosis. Deep learning requires a significant amount of annotated data for training the model. As the radiologists are busy dealing with the pandemic, the annotation task is difficult and costly. Therefore, a weakly supervised technique by [PERSON] et al. [14] utilizes weak  
Figure 2: Samples from the COVIDx [2] dataset. The upper row shows examples with COVID-19, while the lower row presents images without COVID-19’s presence. patient-level labels for the rapid diagnosis of COVID-19 subjects. A 3D deep convolutional neural network called DeCoVNet [14] is used to take the input of the CT volumes and 3D lung masks to output the probabilities of COVID-19 or no COVID-19. A pre-trained model is used to generate a 3D lung mask. The first stage of the architecture consists of a vanilla 3D convolutional base, followed by batch normalization and max-pooling to create a 3D feature map. In the second stage, a 3D feature map is passed through two 3D residual blocks with the batch norm. In the last step, a Progressive Classifier (ProCIf) progressively abstracts the information in 3D volumes and classifies it using the softmax function to output the probability of being COVID-19 or non-COVID-19. Subsequently, [PERSON] and [PERSON] [20] proposed the Visual Geometry Group (VGG) in different variations such as VGG16 and VGG19, which are the most common architectures with 16 and 19 layers, respectively. The typical pattern among these architectures uses only 3 \(\times\) 3 filters.