## 4 Deep Learning for COVID-19 Detection and Classification
### Identification of COVID-19 in CXR Images Using Deep Learning Models  
A substantial body of research has been published recently on models for the detection and classification of COVID-19 using binary/multi-class classifiers for CXR images with off-the-shelf networks. In this review, we chose not to address studies with solutions involving binary classification. This is because the ability of AI systems to differentiate between the different classes has considerably improved, as these systems are able to learn from diverse data belonging to different classes [49]. [PERSON] et al. [50] proposed the use of a CNN called Decompose, Transfer, and Compose (DeTraC); this method helps to deal with any irregularities and the limited availability of annotated CXR images. Their study used data from different sources--80 cases of normal CXR images with 4020 \(\times\) 4892 pixels from the Japanese Society of Radiological Technology (JSRT), along with 105 and 11 cases of COVID-19 and SARS, respectively, with 4248 \(\times\) 3480 pixels. Data augmentation techniques were used to generate 1764 samples from the original limited dataset. The different augmentation techniques included random up/down and right/left flipping, random translation, and rotation using five different angles. A histogram modification technique was applied to the augmented images to enhance the contrast of the images. The augmented dataset was randomly divided into 70% training and 30% validation sets to minimize classification bias. An AlexNet network based on shallow learning was used for the class decomposition layer, and different ImageNet pre-trained CNN networks were used for the transfer learning stage. The high-dimensional feature space was substantially reduced using PCA. The highest accuracy was achieved by VGG19 in DeTraC. The accuracy rate after applying the model was 93.1%. [PERSON] et al. [51] implemented a deep learning model using a dataset containing 6523 CXR images collected from three different CXR image sources. The dataset was labelled with 250 COVID-19 images, 2753 images belonging to patients with other pulmonary diseases, and 3520 normal patients. The preprocessing stage allowed the reduction of the image dimensions to 224 \(\times\) 224 pixels as well as random distribution into training (2000 images), testing (1100 images), and validation (803 images) sets. Data augmentation was performed via random clockwise and counterclockwise rotation by 15 degrees. The proposed approach is based on a threefold method: Firstly, a process for the detection of any type of pneumonia in the CXR image is conducted. Secondly, if the lungs are not normal, then the system tries to classify between COVID-19 and other pneumonia. Finally, in the event of COVID-19 classification, the images are used to identify the area in the CXR that indicates the presence of COVID-19. The researchers applied the VGG-16 CNN model with 16 layers, which yielded an accuracy of 98% for the detection of COVID 19. The limited number of CXR images that exist for COVID-19 research constituted the focus of the study undertaken by [PERSON] et al. [52]. The dataset used in this research was created by Dr. [PERSON] from the University of Montreal; it consists of total 307 CXR images, including 69 from COVID-19 patients, while the remainder belong to normal, bacterial, and viral pneumonia patients. The proposed model consists of two stages: In the first stage, a generative adversarial network (GAN) is used to generate additional images to increase the size of the existing limited dataset. In the second stage, deep transfer learning is used in the training, validation, and testing phases of the proposed model. For their investigation, the researchers selected the following deep transfer learning models: AlexNet, GoogLeNet, and ResNet18. Each of these networks can take an input image of 512 \(\times\) 512 pixels in size.