# Ensemble Deep Learning for the Detection of COVID-19 in Unbalanced Chest X-ray Dataset
### Related Works  
Remarkable progress has been made in the automated detection of COVID-19 in CXRs [9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24]. Several studies in the literature have leveraged deep convolutional neural networks (CNNs) with and without modifications to convincingly predict COVID-19. CNNs are end-to-end learning models that use multiple processing layers to learn and discover classification patterns and features without explicitly extracting the hand-crafted features. In general, there are two types of classification--binary classification and multi-classification--to predict the prevalence of COVID-19 in CXRs. Binary classification aims to discriminate between positive and negative cases of COVID-19 [10; 11; 12; 13; 14]. Nevertheless, binary classification can lead to the misclassification of COVID-19 with other lung diseases such as non-COVID-19 pneumonia, bacterial pneumonia, and tuberculosis (TB). To remedy this issue, researchers have applied multi-classification of COVID-19 from normal, bacterial pneumonia, TB. Multi-classification methods have improved the detection accuracy of COVID-19 [9]. [PERSON] et al. [10] presented deep learning-based COVIDX-Net using seven CNNs to distinguish positive COVID-19 CXRs from negative ones and obtained an F-measure of 89% for normal and 91% for COVID-19 on a dataset of 25 normal and 25 COVID-19 CXRs. However, their dataset is relatively small to build a reliable deep learning-based model. [PERSON] et al. [11] presented a hybrid classifier that uses the pretrained InceptionNet model as a feature extractor and marine predators' algorithms as the feature selection method to find the dominant features among all extracted features. They achieved 98.7% accuracy on dataset1 consisting of 200 COVID-19 positive CXRs and 1675 CXRs, and 99.6% accuracy on dataset2 consisting of 219 COVID-19 positive and 1341 negative COVID-19 CXRs. [PERSON] et al. [12] utilized VGG-19 based COVID-19 detection with and without image augmentation. Their algorithm attained 95% of F-measure on the original dataset consisting of 28 healthy and 70 COVID-19, and 99% of F-measure on the augmented dataset consisting of 500 healthy and 500 COVID-19 CXRs. [PERSON] et al. [13] proposed a deep learning-based COVID_XNet to differentiate between COVID-19 and normal CXRs and to visualize the COVID-19 pathologies. They first applied a set of image preprocessing techniques such as histogram matching, bone suppression, and contrast limited histogram equalization to reduce the image variability and enhance the image quality. Then, the preprocessed images were input to a customized deep learning architecture to identify COVID-19 CXRs. Their proposed algorithm claimed accuracy of 94.43% and AUC of 98.8% on a dataset consisting of 2589 COVID-19 and 4337 normal CXRs. [PERSON] et al. [14] presented the customized CNN, VGG-16, and MobileNet based classifiers to distinguish COVID-19 from normal CXRs. They claimed the accuracy of 98.7% on the combined public and local dataset containing 1210 COVID-19 and 1583 normal CXRs. Several multi-classifications of COVID-19 from normal, pneumonia, and TB have been developed using deep CNNs [15; 16; 17; 18; 19; 20; 21]. [PERSON] et al. [15] first introduced an open-source COVID-Net to identify COVID-19 CXRs using a customized CNN model. Using the COVIDx dataset consisting of CXRs from 266 COVID-19 cases, 8066 normal cases and 5538 cases of non-COVID-19 pneumonia patients, they claimed that COVID-Net achieved a sensitivity of over 80%. [PERSON] et al. [16] presented a VGG-16-based three-fold framework. The first fold classifies between normal and pneumonia CXRs using 3520 healthy and 3003 CXRs of pulmonary diseases, obtaining an accuracy of 96%. Then, the second fold is applied to differentiate COVID-19 pneumonia from non-COVID-19 pneumonia using 250 COVID-19 CXRs and 2753 CXRs of lung diseases, obtaining an accuracy of 98%. The third fold is subsequently carried out to localize the areas of COVID-19 pathologies using Gradient Class Activation Map (Grad-CAM).