# 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} \hline \hline
**Authors** & **Year** & **Dataset** & **No. of Data Points** & **Model** & **Accuracy** \\ \hline \multirow{4}{*}{[PERSON] et al. [28]} & \multirow{4}{*}{2020} & \multirow{4}{*}{JSRT, NLM} & Normal: 191 & \multirow{4}{*}{FC-DenseNet103 for feature extraction + Viral: 20} & \multirow{4}{*}{88.9\%} \\  & & & & & \\  & & & & COVID-19: 180 & \\ \hline [PERSON] et al. & \multirow{4}{*}{2020} & \multirow{4}{*}{COVIDx} & Control: 49.983 & \multirow{4}{*}{Modified COVID-Net} & \multirow{4}{*}{91.67\%} \\  & & & & \\  & & & & & COVID-19: 8573 & \\ \hline [PERSON] et al. [37] & \multirow{4}{*}{2020} & \multirow{4}{*}{Created COVIDx} & COVID-19: 358 & \multirow{4}{*}{COVID-Net} & \multirow{4}{*}{93\%} \\  & & & & & Pneumonia: 5538 & \\ \hline \multirow{4}{*}{[PERSON] et al. [30]} & \multirow{4}{*}{2020} & \multirow{4}{*}{2 Self-built datasets} & Dataset,1 & \multirow{4}{*}{MobileNet} & \multirow{4}{*}{96.78\%—binary classification} \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline [PERSON] et al. [31] & \multirow{4}{*}{2020} & [PERSON] + ChestX-ray8 & COVID-19: 127 & \multirow{4}{*}{DarkNet} & \multirow{4}{*}{98.08\%—binary classification} \\  & & & & Pneumonia: 500 & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline \multirow{4}{*}{[PERSON] et al. [40]} & \multirow{4}{*}{2020} & \multirow{4}{*}{Built own datasets} & Dataset\_1 & \multirow{4}{*}{MobileNet} & \multirow{4}{*}{94.72\%—3-class classification} \\  & & & & COVID-19: 200 & & \\  & & & & Normal: 1675 & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline \multirow{4}{*}{[PERSON] et al. [8]} & \multirow{4}{*}{2021} & \multirow{4}{*}{Built own datasets} & \multirow{4}{*}{Bacterial Pneumonia: 711 + 4848 augmented} & \multirow{4}{*}{Voting ensemble, ResNet-50, 711 + 4848 augmented} & \multirow{4}{*}{75.23\%—3-class classification} \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & \\  & & & & & \\  & & & & & \\ \hline \multirow{4}{*}{[PERSON] et al. [39]} & \multirow{4}{*}{2021} & COVIDx, MIRR-RICORD-1C, BIMCV & COVID-19: 3913 & \multirow{4}{*}{COVID (modified) Normal: 13,417} & \multirow{4}{*}{95\%} \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline \multirow{4}{*}{[PERSON] et al. [34]} & \multirow{4}{*}{2021} & COVIDx & COVID-19: 183 & \multirow{4}{*}{EfficientNet} & \multirow{4}{*}{96.8\%} \\  & & & & Pneumonia: 5521 & \\  & & & & Normal: 8066 & \\ \hline \multirow{4}{*}{[PERSON] et al. [33]} & \multirow{4}{*}{2021} & \multirow{4}{*}{Local dataset + public dataset} & Public dataset: COVID-19: 713 & \multirow{4}{*}{MobileNet} & \multirow{4}{*}{99\%} \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & \\  & & & & \\ \hline \multirow{4}{*}{[PERSON] et al. [43]} & \multirow{4}{*}{2021} & Kaggle Dataset and GitHub Dataset & COVID-19: 3616 & \multirow{4}{*}{Decuset201, 12} & \multirow{4}{*}{99.23\%} \\  & & & Pneumonia: 1345 & \multirow{4}{*}{InceptionResNetV2} & \multirow{4}{*}{99.23\%} \\  & & & & & \\  & & & Normal: 10,192 & & \\  & & & & & \\  & & & & \\ \hline \multirow{4}{*}{[PERSON] et al. [42]} & \multirow{4}{*}{2021} & \multirow{4}{*}{Kaggle Dataset} & COVID-19: 2313 & \multirow{4}{*}{Xception + Pneumonia: 2313} & \multirow{4}{*}{Chouquet Fuzzy} & \multirow{4}{*}{99.57\%} \\  & & & & & \\  & & & & & \\  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of work using deep learning for COVID-19 classification through X-rays.