# Investigating the Performance of FixMatch for COVID-19 Detection in Chest X-rays
###### Abstract  
The advent of the COVID-19 pandemic has resulted in medical resources being stretched to their limits. Chest X-rays are one method of diagnosing COVID-19; they are used due to their high efficacy. However, detecting COVID-19 manually by using these images is time-consuming and expensive. While neural networks can be trained to detect COVID-19, doing so requires large amounts of labeled data, which are expensive to collect and code. One approach is to use semi-supervised neural networks to detect COVID-19 based on a very small number of labeled images. This paper explores how well such an approach could work. The FixMatch algorithm, which is a state-of-the-art semi-supervised classification algorithm, was trained on chest X-rays to detect COVID-19, Viral Pneumonia, Bacterial Pneumonia and Lung Opacity. The model was trained with decreasing levels of labeled data and compared with the best supervised CNN models, using transfer learning. FixMatch was able to achieve a COVID F1-score of 0.94 with only 80 labeled samples per class and an overall macro-average F1-score of 0.68 with only 20 labeled samples per class. Furthermore, an exploratory analysis was conducted to determine the performance of FixMatch to detect COVID-19 when trained with imbalanced data. The results show a predictable drop in performance as compared to training with uniform data; however, a statistical analysis suggests that FixMatch may be somewhat robust to data imbalance, as in many cases, and the same types of mistakes are made when the amount of labeled data is decreased.  
COVID-19; chest X-rays; deep learning; semi-supervised learning; FixMatch +
Footnote †: journal:  
+++ ==WARNING: Truncated because of repetitions==
automated diagnosis of COVID-19 through computer chest X-rays (CXRs) has become a popular topic [7]. As machine learning techniques required labeled data, authors such as [PERSON] et al. [8] have provided large public datasets with a collection of COVID-19 related CXRs. However, it took almost 2 years after the start of the pandemic for such datasets to appear. This is because, in situations where a novel disease is discovered, compiling and building datasets containing adequate amounts of labeled data relating to the disease consumes considerable time and effort [9]. This is additionally taxing as the datasets generally have to be compiled by medical experts whose time would be better utilized in actively helping to contain the spread of any such diseases. Consequently, such situations, semi-supervised learning can be leveraged. As opposed to requiring large amounts of labeled data, semi-supervised algorithms are generally able to effectively generalize data classes based on only a handful of representative samples [10]. Therefore, it may be worthwhile to apply such techniques to the problem of COVID-19 detection from CXR images. This paper investigates how well the currently best performing semi-supervised learning algorithm called FixMatch by [PERSON] et al. [11] performs at detecting COVID-19 based on X-ray images. The contributions of this paper are as follows:  
1. Providing a comprehensive literature review of using X-ray data to detect various COVID-19 types of diseases.
2. Evaluating the performance of InceptionV3, DenseNet121, Xception, ResNet50 and EfficientNet (B1, B2 and B3) to detect COVID-19 by using transfer learning.
3. Evaluating FixMatch algorithm to detect COVID-19 by using varying percentages of labeled data.
4. Evaluating the robustness of the FixMatch algorithm against data imbalance for detecting COVID-19 by varying the distribution of majority/minority classes and percentage of labeled data.  
The following sections discuss various approaches to the problem of CXR classification seen in the literature before outlining the experimental methodology and discussing the results obtained.