# Multi-Classification of Chest X-rays for COVID-19 Diagnosis Using Deep Learning Algorithms
## 2 Related Work
A total of 4560 CXR images of patients with COVID (360 cases), as well as viral, bacterial, fungal, and other diseases, were used. Their model detection accuracy was compared to the performances of 10 existing COVID-19 identification techniques and achieved an accuracy of 98.5% in the 2-class classification task. The advantage of their method is that the utilization of different filters improved the performance of the classifiers. The limitations of their study is that the dataset used was small. A 2-way classification framework for detecting 15 different types of chest diseases including COVID-19 using CXR images was proposed by [PERSON] et al. [24]. First, a CNN architecture with a softmax classifier was used. Then, TL was used with a fully connected layer of the proposed CNN to extract deep features. Deep features were then fed into traditional ML classifier. They performed 10-fold and 5-fold validation on the best-performing of the 7 ML classifiers. They collected 2800 CXR images belongs to 14 classes and 200 CXR images belonging to the COVID-19 class. Their method achieved an overall validation accuracy of 99.40% with the KNN-fine algorithm using 5-fold validation and and 99.77% with the Bag-ensemble algorithm using 10-fold validation. Although their method has the advantages of being a fusion between the deep and machine learning models, the dataset used was small, and their method of training took a long time (500 epochs). In [25], [PERSON] et al. designed a ResNet50 CNN-based architecture for detecting and classifying four types of classes using CXRs. Their dataset consisted of 21,165 CXR images divided into 6012 images for lung opacity, 3616 images for COVID-19, 1345 images for viral pneumonia and 10,192 images for the normal class. Their approach scored a test accuracy of 94% using the 5-fold cross validation technique. The advantage of this study is the large dataset used to compare different pre-trained models to detect the most suitable model. The limitation is the large number of epochs (100) used for training with the limited computing needed to perform hyper-parameter space searches, and, as a result, it took a long time. Additionally, the output accuracy was not high, although the dataset used was large and the principle of the TL was applied. To alleviate the burden placed on a single network, a multi-step classification by [PERSON] et al. was proposed in [26] for detecting COVID-19 and other chest diseases using X-ray images. They applied the TL with different pre-trained models using data augmentation and semantic segmentation in order to increase the model's accuracy in a 10-fold cross-validation. For the first level of classification (i.e., 3 classes), their technique achieved an average test accuracy of 92.52%. In the second level of classification (i.e., 14 classes), using a ResNet50 model, their technique achieved a maximum test accuracy of 66.63%. For all 16 classes, which were classified at once, the overall accuracy for COVID-19 detection decreased, achieving a rate of 71.91%. The advantage of this study is that the combination between the two classifiers provided a compatible accuracy on the dataset used, and the dataset contained more than two classes. The limitations of this study are that the accuracy was not high despite applying the TL and data augmentation techniques, and the dataset used was of a small size. [PERSON] et al. proposed a modified DenseNet201 network[27] for COVID-19 detection that included a global averaging layer, a batch normalization layer, a dense layer with ReLU activation, and a final classification layer. Their model was trained using 8644 images (4000 normal and pneumonia cases and 644 COVID-19 cases) and tested on 1729 images (129 COVID-19, 800 normal, and 800 pneumonia) and yielded an overall accuracy of 92.19% compared to 7 pre-trained models. The advantage of this study is that the employment of the TL technique provided an acceptable accuracy.