# Investigating the Performance of FixMatch for COVID-19 Detection in Chest X-rays
## 2 Literature Review
### COVID-19 Detection Using Deep Learning
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Authors** & **Year** & **Dataset** & **No. of Data Points** & **Domain** & **Model** & **Results** \\ \hline \multirow{8}{*}{[PERSON] et al. [19]} & \multirow{8}{*}{2016} & \multicolumn{6}{c}{Kit} & \multirow{8}{*}{\begin{tabular}{c} CNN based on \\ dataset: 90.3\% \\ \end{tabular} } \\  & & & TB: 3828 & & & \\  & & & Normal: 7020 & & & \\  & & & MC & & & \\ [PERSON] et al. [19] & 2016 & \begin{tabular}{c} KIT, MC, \\ Shenzhen \\ \end{tabular} & \begin{tabular}{c} TB: 58 \\ Normal: 80 \\ Zhen \\ TB: 336 \\ \end{tabular} & Tuberculosisosis & \begin{tabular}{c} CNN based on \\ AlexNet \\ \end{tabular} & \begin{tabular}{c} Average accuracy \\ across three \\ datasets: 90.3\% \\ \end{tabular} \\ \hline \multirow{4}{*}{[PERSON] et al. [21]} & \multirow{4}{*}{2017} & \multirow{4}{*}{Private dataset} & \begin{tabular}{c} Normal: 453 \\ Different \\ manifestations of TB: \\ \end{tabular} & \begin{tabular}{c} Multi class \\ Tuberculosisosis \\ \end{tabular} & AlexNet & Accuracy: 85.86\% \\ \cline{1-1} \cline{2-6}  & & Kaggle dataset for & & & & \\ [PERSON] et al. [22] & 2020 & \begin{tabular}{c} Kaggle dataset for \\ segmentation, \\ NLM, Belarus, \\ NIAID, RSNNA \\ for classification \\ \end{tabular} & \begin{tabular}{c} TB: 3500 \\ Normal: 3500 \\ for classification \\ \end{tabular} & Tuberculosisosis & \begin{tabular}{c} Unet for \\ segmentation, \\ DenseNet201 for \\ classification \\ \end{tabular} & Accuracy: 98.6\% \\ \cline{1-1} \cline{2-6}  & & & & & & \\ [PERSON] et al. [27] & 2021 & ChestXRay2017 & 5856 CXRs & \begin{tabular}{c} Pneumonia, \\ Tuberculosisosis \\ \end{tabular} & \
\begin{tabular}{c} AUC for \\ feature extractor \\ + ResNet50 \\ \end{tabular} &
\begin{tabular}{c} AUC for \\ AUC for \\ Tuberculosisosis: \\ 0.923 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of work using deep learning for Tuberculosis classification through CXRs. CheXNet model was trained by using the ChestX-ray14 dataset, and its weights were fit for classification by using chest X-rays. The authors were able to achieve an accuracy of 90.5% on the public covid-chestray-dataset Similarly, [PERSON] et al. [30] used two self-built datasets for experimenting, using transfer learning. The authors tried VGG19, MobileNet v2, Inception, Xception and Inception ResNet v2. They found that MobileNet performed the best, even with the small dataset size, giving an accuracy of 96.78% for binary classification and 94.72% for three-class classification. Similarly, [PERSON] et al. [31] used DarkNet as the basis for their model. They trained and tested on a combination of two datasets and experimented by using a binary classifier and a multiclass classifier to detect COVID-19, normal and Pneumonia. The binary classifier achieved an accuracy of 98.08%, while the three-class classifier only achieved an accuracy of 87.02%. [PERSON] et al. [32] created three datasets from a combination of images from Kaggle and GitHub, with the first dataset consisting of 341 COVID and 2800 normal images, the second having 341 COVID and 1493 Viral Pneumonia images and the third having 341 COVID and 2772 Bacterial Pneumonia. The best model was found to be ResNet50, with an accuracy of 96.1% for dataset 1, 99.5% for dataset 2 and 99.7% for dataset 3. [PERSON] et al. [33] used a combination of locally acquired images and a publicly available dataset for their experimentation. The authors tested a simple 2D-CNN, MobileNet and VGG-16 with different combinations of the datasets. For the 2D-CNN, the public dataset alone performed best, with an accuracy of 96.1%, while for MobileNet, the fused dataset for training and the local dataset for testing achieved an accuracy of 98.7%. Finally, the same dataset combination as MobileNet performed the best, with an accuracy of 99% with VGG-16. [PERSON] et al. [34] used transfer learning on the latest state-of-the-art EfficientNet model with the COVIDx dataset. They were able to achieve an overall accuracy of 93.9%, with COVID-19 sensitivity of 96.8%.