# Ensemble Deep Learning for the Detection of COVID-19 in Unbalanced Chest X-ray Dataset
### Related Works
[PERSON] et al. [17] proposed ReCoNet (residual image-based COVID-19 detection network) to classify COVID-19 CXRs from normal and non-COVID-19 pneumonia. ReCoNet is a deep learning framework that applies multi-level filtering blocks for preprocessing images followed by multilevel feature extraction layers and classification layers. Their proposed network was evaluated using a combined dataset consisting of 238 COVID-19, 8851 normal, and 6045 pneumonia CXRs,and attained an overall accuracy of 97.48%. [PERSON] et al. [18] introduced a deep-learning-based decision tree classifier to recognize COVID-19 CXRs from normal and pneumonia images. The first tree classified between normal and abnormal on a dataset of 585 normal and 585 abnormal CXRs and obtained an accuracy of 98%. The second decision tree further differentiated the abnormal CXRs into TB or non-TB on a dataset consisting of 492 images per class and achieved an accuracy of 80%. The final decision tree differentiated between COVID-19 and TB on a dataset of 142 images per class and attained an average accuracy of 95%. [PERSON] et al. [19] presented a variant of the Darknet-19 model with fewer layers and filters to automatically predict COVID-19. Their model was trained as a binary classifier (COVID-19 vs. no findings) and a multi-class classifier (COVID-19 vs. non-findings and pneumonia). Using the dataset consisting of 125 COVID-19, 500 normal, and 500 pneumonia CXRs, the binary classifier achieved 98.08% accuracy, whereas the multi-class classifier achieved 87.02%. [PERSON] et al. [20] compared the performance of 16 pretrained CNN models and combined the best-performing methods as an ensemble classifier to enhance the accuracy. They achieved the highest accuracy of 99% with the ensemble classifier by using majority voting on the dataset of 237 COVID-19, 1338 normal, and 1336 viral pneumonia CXRs. [PERSON] et al. [21] presented a three-stage COVID-19 detection and severity scoring metric. The first stage used VGG-16 to differentiate between normal, pneumonia and TB, and achieved 95.9% accuracy. Then, the second stage further classified the pneumonia CXRs into COVID-19 pneumonia and non-COVID-19 pneumonia using DenseNet-161, obtaining 98.9% accuracy. The final stage used ResNet-18 to grade the COVID-19 pneumonia CXRs into mild, medium, and severe, with a test accuracy of 76%. Except for [13; 14], previous studies have applied deep learning on the dataset with a very limited number of COVID-19 CXRs, fewer than 300 images. Deep learning algorithms need massive amounts of data to learn the information and patterns for classification; therefore, using a small dataset might hinder the efficiency of these studies. To mitigate the problem of a limited number of COVID-19 CXRs, [PERSON] et al. [22] and [PERSON] et al. [23] proposed algorithms which work well with limited datasets. [PERSON] et al. [22] proposed a patch-based deep learning method for the classification of COVID-19 CXRs from normal, non-COVID-19 pneumonia and TB images on a limited training dataset. Initially, the images are preprocessed, and subsequently, lung regions are segmented using FC-DenseNet. The segmented images are partitioned into small patches and fed to ResNet-18 to predict COVID-19. Finally, the heat saliency maps are superimposed on the segmented lung regions to spot biomarkers. Their algorithm was assessed on a combined dataset of six public datasets consisting of 180 COVID-19, 191 normal, 74 pneumonia and 57 TB CXRs, and attained a sensitivity of 92.5%. For multi-classification of COVID-19, pneumonia, and normal CXRs, [PERSON] et al. [23] presented several fine-tuned CNNs which were optimized with the grid search method. Lung regions were segmented and cropped using U-Net segmentation. The models were then iteratively pruned to reduce the model complexity and to deal with the limited number of images. Finally, the best-performing pruned models were combined as the ensemble classifier.