# COVID-19 Diagnosis in Chest X-rays Using Deep Learning and Majority Voting
## 1 Introduction
### Related Works
[16] proposed a deep convolutional neural network design (COVID-Net), to detect the COVID-19 cases from chest radiography X-ray images. They used the open-source COVIDx dataset; it comprises 16,756 chest radiography images from 13,645 patient cases from two open access data repositories. More specifically, the COVIDx dataset contains only 76 radiography images from 53 COVID-19 patient cases, while there are significantly more patient cases and corresponding radiography images with Normal and Non-COVID-19 pneumonia. In total, there are 8066 normal patient cases and 5526 cases of non-COVID-19 pneumonia patients. The COVID-Net network architecture uses a lightweight residual projection-expansion-projection-extension (PEPX) design pattern. The first-stage projection is composed of \(1\times 1\) convolutions for projecting input features to a lower dimension. The expansion stage is composed of \(1\times 1\) convolutions for expanding features to a higher dimension that is different than that of the input features. The Depth-wise representation uses efficient \(3\times 3\) depth-wise convolutions for learning spatial characteristics to minimize computational complexity. The second-stage projection is composed of \(1\times 1\) convolutions for projecting features back to a lower dimension. Finally, an extension is composed of \(1\times 1\) convolutions that extend channel dimensionality to a higher dimension to produce the final features. The COVID-Net balances accuracy and computational complexity by achieving 92.4% test accuracy, while requiring just 2.26 billion MAC (Multiplier Accumulator)operations to perform case predictions. In another approach, [PERSON] et al. [17] proposed to diagnose COVID-19 cases from X-ray images using a set of pre-processing algorithms followed by a designed CNN (COVID-XNet) in order to distinguish COVID-19 cases from normal cases at an average accuracy of 94.43% and an AUC (Area Under Curve) of 0.988. They also went deeper by analyzing the extracted features from COVID-XNet using the Class Activation Maps (CAM). This helped to localize precisely the COVID-19 infected areas inside the screened lungs. The localization accuracy was assessed qualitatively by a lung specialist and confirmed to be efficient and accurate. Leveraging fractional-order (FO) calculus techniques, [PERSON] et al. [18] proposed a COVID-19 X-ray classification method that uses a pre-trained CNN (Inception [19]) as feature extractor, followed by an improved swarm-based meta-heuristic optimization technique (Marine Predators Algorithm [20]) to select only relevant features. They achieved up to 99.6% accuracy on the binary classification problem on the dataset made public by [PERSON] et al. [21]. While we used the same dataset, the problem that we address in this paper is more challenging since it also considers a third class of other pneumonia cases. More recently, in order to mitigate the problem of the small size of available COVID-19 datasets, [PERSON] and [PERSON] [22] used a conditional generative adversarial network (cGAN [23]) for data augmentation. Accordingly, they generated realistic synthetic images only for the under-represented COVID-19 class, since the two other classes had a sufficient number of original images. Then, they tested two ad hoc lightweight deep-learning models on the augmented dataset. They obtained an accuracy of 98.7% and 98.3% on the binary and 3-class problems, respectively, on a small balanced dataset (275 images for each class) that they extracted from originally unbalanced datasets made public by References [24,25]. In a similar approach, [PERSON] and [PERSON] [26] used a different type of generative adversarial network (a CycleGAN [27]) for augmenting the number of COVID-19 images, then tested several pre-trained CNN-based feature extractors. They attained a maximum accuracy of 96.8% with EfficientNet-B0 architecture [28] on a selected dataset where COVID-19 images were taken from Reference [24], while normal and other pneumonia images were taken from Reference [25].