# Investigating the Performance of FixMatch for COVID-19 Detection in Chest X-rays
## 2 Literature Review  
### X-ray Classification Using Deep Learning  
As pointed out by [PERSON] et al. [6], the most common work performed in the field of X-ray classification is diagnosing Pneumonia and Tuberculosis (TB). A common dataset used for the Pneumonia problem is the ChestX-ray14 dataset that consists of over 100,000 images and 14 class labels. The cheXNet model by [PERSON] et al. [12] that used transfer learning for diagnosis using X-rays. CheXNet is an adaptation of DenseNet [13], which was trained on the ChestX-ray14 dataset. The weights were initialized with the ImageNet weights. For pneumonia detection, the Pneumonia labeled images were taken as the positive class, and all other labels were considered as the negative class. To test the model, an unseen dataset of 420 images from four practicing radiologists at Stanford University was used. The model was also extended to classify all 14 labels and achieved state-of-the-art results at that time, with an example of 0.9164 Area Under the Curve (AUC) for detecting Hernia and 0.7680 AUC for detecting Pneumonia. This was an improvement on [PERSON] et al. [14], who found the best model to be ResNet-50 [15] trained on a ChestX-ray8 dataset that consisted of over 32,000 unique patients with eight disease labels had an AUC of 0.63 for Pneumonia classification. [PERSON] et al. [16] used various ResNet models and found that ResNet-38 improved the results further with an example of AUC of 0.937 for Hernia and 0.714 for Pneumonia. [PERSON] et al. [17] used three models for transfer learning, namely ResNet-50, Inception V3 [18] and DenseNet121. DenseNet121 performed the best, with and AUC of 0.71. The model was also tested on an unseen CheXpert dataset, and DenseNet121 performed the best, again, with an AUC of 0.76. One of the first works to automate the detection of Tuberculosis (TB) through chest X-rays was performed by [PERSON] et al. [19]. The CNN architecture based on AlexNet [20], using transfer learning with low-level filters, worked the best, with an average accuracy of 90.3% across three datasets, which was a significant improvement from 77.3% when trained without transfer learning. The dataset used for training was the KIT dataset that consisted of 7020 normal and 3828 TB images retrieved from the Korean National Tuberculosis Association. [PERSON] et al. [21] trained their model on a private dataset that consisted of six variations of TB. There was a total of 453 normal and 4248 TB images. This work also utilized AlexNet, with an accuracy of 85.86%. The model was first trained by using the original weights and then fine-tuned. They also trained a binary classifier which achieved an average accuracy of 97.82% across 5-fold evaluation. More recently, [PERSON] et al. [22] proposed using segmentation to extract the lung features as an input. Consequently, U-net [23] was used for the segmentation, and after trying nine different deep CNN models, it was found that ChexNet performed the best, with an accuracy of 96.47%, without segmentation; and DenseNet201 performed the best, with an accuracy of 98.6%, with segmentation, and this was an improvement from before. The dataset used to train the lung segmentation was a Kaggle chest X-ray dataset that consisted of 704 images, along with their corresponding lung masks, while NLM, Belarus, NIAID and RSNNA were used for the classification network with 3500 images of each of the binary classes. Semi-supervised learning (SSL) has also been used to address Pneumonia detection problem using X-rays. For example, [PERSON] et al. [24] proposed an SSL-based model utilizing Generative Adversarial Networks (GANs) [25]. GANs are used, along with transfer learning, utilizing a pretrained VGG16 model [26] as the discriminator. A few of the layers of the VGG model were unfrozen while training for fine-tuning the model toward X-ray images. The SGAN model not only generates new fake images but also learned to classify unlabeled images into the correct classes.