# Multi-Classification of Chest X-rays for COVID-19 Diagnosis Using Deep Learning Algorithms
## 2 Related Work  
Recent research work has proven that imaging tests (e.g., CXRs and CT) can provide rapid identification of COVID-19 and also help to monitor the spread of the disease. Various image-based diagnostic systems, ranging from hand-crafted features to feature learning, have been introduced to illustrate the possibility of the identification of COVID-19 [13]. Convolutional neural networks (CNN) have been found to be one of the most common and successful techniques for diagnosing COVID-19 from medical images. [PERSON] and [PERSON] [14] used deep TL techniques for a multi-classification task on a small dataset by testing 15 different pre-trained models. Their dataset consisted of 860 images (260 for COVID-19, 300 normal and 300 for pneumonia). Their study revealed that VGG19 was the best algorithm, achieving a classification accuracy of 89.3% with average precision, recall, and F1-Score values of 0.90, 0.89, 0.90, respectively. [PERSON] et al. [15] proposed an approach based on a convolution support estimation network (CSEN), which constructs a sparse support set of representation coefficients using a dictionary and a set of training samples. The dataset used included 6286 CXR images of COVID-19, as well as three other classifications: bacterial pneumonia, viral pneumonia, and normal. Using a 5-fold cross validation, their CSEN-based model achieved a sensitivity and specificity of more than 98% and 95%, respectively, for COVID-19 detection. A generative adversarial network (GAN)-based method was presented by [PERSON] et al. [16] to detect COVID-19 infection from CXR images. Their dataset consisted of 307 images divided into 4 classes (COVID-19, normal, bacterial pneumonia, and viral pneumonia). They used AlexNet, GoogleNet, and ResNet18 algorithms as TL. Their model achieved an accuracy of 80.6% on 4 classes (GoogleNet), 85.3% on 3 classes (AlexNet) and 100% on 2 classes (GoogleNet). A similar approach by [PERSON] et al. [17] combined TL and the Xception pre-trained model. Their system attained a 89.6 % and 95% accuracy on classification for 4 and 3 classes, respectively. Another DCNN transfer learning-based pipeline by [PERSON] et al. [18] utilized Inception V3 for the detection of COVID-19 in infected patients using chest X-ray scans. The test data contained 864 scans for COVID-19, 1345 for viral pneumonia and 1341 for normal scans. The model provided a classification accuracy of greater than 98% (training accuracy of 97% and validation accuracy of 93%). [PERSON] and [PERSON] [19] produced a convolutional capsule network using chest scans for COVID-19 detection called CapsNet. Their system was evaluated using a total of 2331 images, of which 231 were COVID-19 and 1050 were normal and pneumonia. For binary and multi-class classification, the 11-layer architecture achieved 97.24% and 84.22% accuracy, respectively. [PERSON] et al. [20] used the ResNet50 plus support vector machine (SVM) model as a feature extractor in a framework for identifying COVID-19 patients. They proposed a multi-classification task on 381 CXR images. Their model achieved an accuracy, sensitivity, FPR and an F1-Score of 95.33%, 95.33%, 2.33% and 95.34%, respectively. The limitation of their method is the leakage of the used dataset. In [21], the DenseNet121 model was used by [PERSON] et al. for both binary and multi-classification tasks of COVID-19 patients on a dataset consisting of 238 images for COVID-19, 6045 for pneumonia and 8851 for normal persons. Classification results of 2 and 3 classes obtained 96.49% and 93.71% accuracy, respectively. A study by [PERSON] et al. [22] compared several types of TL with their proposed model for a multi-classification task of COVID-19 on a dataset that included three classes: COVID-19, pneumonia and other disease patients. Their model achieved a test accuracy of 97.4% between the used models. [PERSON] et al. [23] proposed a model for screening for COVID-19 infection using a GAN DL approach. Data augmentation were employed using different filter banks.