# Investigating the Performance of FixMatch for COVID-19 Detection in Chest X-rays
## 2 Literature Review
### COVID-19 Detection Using Semi-Supervised Deep Learning  
[PERSON] et al. [44] proposed a teacher-based network where an initial CNN model was trained to generate pseudo labels for the unlabeled images, and then a classification CNN model was used to do the final classification, which was trained by using the original and pseudo labels. The authors combined three datasets, namely COVIDx, BIMCV and MIDRCRICORD, and the final dataset consisted of 3795 COVID cases, 6045 Pneumonia cases and 8851 normal cases. An equal distribution of each group was selected, and 20%, 30% and 40% labeled samples were used. From this experimentation, they found that the set with 40% labeled sampled performed the best, with an F1 score of 0.93. The 30% labeled samples were able to achieve the same F1-score of 0.91 as a fully supervised XNet model trained on the same dataset.  
Some of the other works utilized a state-of-the-art SSL model named MixMatch [45] for the classification of COVID-19, with lower amounts of labeled data. For example, [PERSON] et al. [46] used MixMatch with a wide-Resnet classifier. The author used a dataset from a GitHub repository and selected 102 positive and negative images. The author then experimented on the model with 25, 40, 50, 70 and 100 labeled images. By training on the full imbalanced dataset of 4468 images, the author achieved an accuracy of 96.6%, while training with a selective number of labels saw its peak accuracy of 85.1% at 100 labeled images, as was expected, as there were only two unlabeled for each class. The authors further improved their accuracy, as seen in Reference [47], by using a Densenet121 model for the classifier with MixMatch. An additional unseen dataset was created for testing. Sets of 10, 15 and 20 labeled samples were tested with varying proportions of imbalance for the labeled set. The authors tested both binary classification and multiclass classification. The final model yielded an accuracy of 91.3% as compared to the accuracy of 67.74% for the fully supervised model. For the chest X-ray8 dataset, an accuracy of 93.4% was achieved.  
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Another example for the usage of SSL in the detection of COVID-19 through chest X-rays is when unlabeled images were used to train a feature extractor instead of generating pseudo labels. For example, [PERSON] et al. [48] proposed a three phase model for the classification of COVID-19. They compiled their own dataset with 50,000 unlabeled X-rays and a heavily imbalanced set of images including labels of COVID-19, normal, SARS and Pneumonia. First, an autoencoder was used to extract the deep local features for the unlabeled generic chest X-ray set, and then transfer learning was used to train the main model with ImageNet weights; finally, downstream training was performed to convert from generic to COVID-19 detection. They tested the model on two COVID-19 datasets, with the first dataset achieving an accuracy of 97.54% and the second dataset achieving an accuracy of 99.8%.  
[PERSON] et al. [49] proposed a method of transfer learning in a self-supervised task-agnostic way, utilizing the CheXpert dataset for training, and then the model was tested on four datasets, namely Cell, ChestX-ray14, C19-Cohen and COVIDGR. The self-learning method is similar to the mechanism of SimCLR and MoCo, where data augmentation was used to make two versions of each image in a batch, where a positive pair is derived from the same image and a negative pair is between different images. The backbone network for this work was the ResNet50 Wide Network. The system was also tested with three different data fractions, namely 1%, 10% and 100%. For COVID detection, in the COVIDGR dataset, the 100% fraction gave the best result, 97.1% AUC; meanwhile, for the C19-Cohen dataset, the 10% fraction gave the best AUC, 91.5%.  
Table 4 below summarizes the work reviewed for the above section.