# Investigating the Performance of FixMatch for COVID-19 Detection in Chest X-rays
## 6 Conclusions and Future Work  
The COVID-19 pandemic is not over yet, and people are facing difficulties due to the shortage of testing kits; hence, an alternative testing method is required CXRs can be used to detect COVID-19, as it has been seen that it could find COVID-19 in cases where the patients had symptoms, yet the PCR test returned a negative result. To reduce the burden on radiologists, deep learning can be applied for the automated detection of the disease. One limitation of the present datasets is that the data are heavily imbalanced, since the pandemic is fairly recent, and a lot of information has yet to be documented. To solve this issue, semi-supervised learning can be utilized, as these algorithms are able to generalize data with lower amounts of representative samples.  
This paper therefore explored the efficacy of the state-of-the-art FixMatch semi-supervised algorithm for this problem and benchmarked the obtained results for different proportions of labeled data against supervised CNN models by utilizing transfer learning.  
The results demonstrate that, even with a small proportion of labeled data, the FixMatch model is able to perform adequately well--indeed, almost along the best supervised techniques. Furthermore, an exploratory analysis was conducted toward investigating the effect of an imbalanced training dataset on the FixMatch model. The obtained results suggest that, while there is an expected drop in performance as the level of imbalance is increased, the drop is somewhat consistent and not exponential, as may be expected, thereby suggesting that the FixMatch algorithm could be somewhat robust to high levels of data imbalance.  
Future investigations could explore the efficacy of other prevalent semi-supervised learning algorithms in the domain of COVID-19 detection from CXRs. Furthermore, a deeper analysis on the effects of data imbalance on the training could be carried out in order to accurately understand the behavior of the models when faced with such data. Such an experiment could lead to attempts to improve the robustness of such models for cases of high imbalance. In addition to the discussed works aiming to mitigate the effects of data imbalance, a possible class of techniques to be explored in this domain is those involving cost-based approaches being applied as part of the training process, such that the minority classes are given more importance. The enhancement of the performance of such techniques for imbalanced data will serve to improve the field in general, as such semi-supervised models will be able to generalize newer diseases for which inadequate data are present, therefore aiding medical professionals in their battle against any such outbreak.  
Conceptualization, I.Z.; methodology, A.R.S.; software, A.R.S.; validation, A.R.S.; formal analysis, A.R.S. and I.Z.; investigation, D.S.; resources, D.S.; data curation, A.R.S.; writing--original draft preparation, A.R.S. and D.S.; writing--review and editing, I.Z.; visualization, A.R.S.; supervision, I.Z. All authors have read and agreed to the published version of the manuscript.  
The work in this paper was supported, in part, by the Open Access Program from the American University of Sharjah [grant number: OAPCEN-1410-E00064].  
Not applicable.  
This paper represents the opinions of the authors and does not mean to represent the position or opinions of the American University of Sharjah.  
The authors declare no conflict of interest.