# Investigating the Performance of FixMatch for COVID-19 Detection in Chest X-rays
## 2 Literature Review
### COVID-19 Detection Using Deep Learning
[PERSON] et al. [35] extended the pretrained Xception model by adding an addition global average pooling (GAP) layer to avoid overfitting. The authors trained their model with over 7000 images from multiple Kaggle datasets for detecting three classes, COVID-19, Pneumonia and normal and were able to achieve a testing accuracy of 99.3%. Finally, [PERSON] et al. [36] found the best transfer learning model to be InceptionResNetv2 out of the five models they tested. As the original dataset only had 500 images, the authors decided to generate synthetic COVID-19 CXRs by using a CGAN. They were able to achieve and accuracy of 99.72%. While the majority of works used transfer learning for identifying COVID-19 through chest X-rays, custom designed models have also been explored. One such model is COVID-Net implemented by [PERSON] et al. [37]. They developed their own dataset COVIDx which was used by in the future. This dataset is relatively large in comparison to other datasets comprising of 358 COVID-19, 8066 normal and 5541 pneumonia chest X-rays. The model used a lightweight residual projection-expansion-projection-extension design pattern and consists of one convolution layer to expand the features into a higher dimension, three depth-wise convolutions or learning spatial characteristics, one convolutional layer for projecting back to the lower dimension and finally a single convolutional layer to get the final features. Their model achieved an average accuracy of 93%. Similarly, [PERSON] et al. [38] built on this model for their experimentation. They modified the last two dense layers and added a weighted categorical cross-entropy loss for the compensation of the class imbalance. Multiple datasets are combined to create a dataset with more than 79,500 X-ray images with over 8500 COVID-19 samples. The authors experimented with three settings: directly using the raw data, using cropped images and using lung segmentation. From this experimentation, it was found that raw data performed the best, with an accuracy of 91.67%, while segmentation came close with an accuracy of 91.53%. The cropped images performed the worst, with an accuracy of 87.64%. The COV-SNET model by [PERSON] et al. [39] improved upon this accuracy. They used the COVIDx dataset for their experimentation. Due to the high imbalance of the dataset, the authors decided to do a 90-10 train-test split. For testing, the authors matched the number of images within the COVID-19 category. The authors also built a secondary training set consisting of 3913 COVID-19 images. The additional images were extracted from the MIDRC-RICORD-1C dataset and BIMCV dataset. The authors improved upon the DenseNet-121 network by adding an additional dense layer with 128 units and a dropout layer, followed by a softmax layer for multiclass classification and a dense layer with a sigmoid activation function for the binary classifier. Initially, the newer layers were trained, and then the full model was unfrozen. Both the three-class and two-class classifiers achieved an average accuracy of 95%. Finally, [PERSON] et al. [40] proposed the usage of modified CNN by retraining a pretrained Inception model, along with the Fractional-Order Marine Predators Algorithm for better feature extraction. The authors built two of their own datasets by combining various sources, as well as extracting normal X-rays from Kaggle. Both the datasets had similar characteristics; for example, the age group of patients was limited to 40-84 years, and the imbalance within the classes were very high, with COVID-19 being the minority. This new approach achieved an accuracy of 98.21% on dataset 1 and 99.1% on dataset 2. The use of ensemble networks was also experimented with to detect COVID. [PERSON] et al. [41] proposed using ensemble network for the classification of COVID-19 through X-rays. This method achieved an accuracy of 98.28% and was able to identify three classes being COVID-19, Pneumonia and normal. They also trained a binary classifier and was able to achieve an accuracy of 99.71%.