# COVID-19 Diagnosis in Chest X-rays Using Deep Learning and Majority Voting
## 1 Introduction
### Related Works  
In the literature, there are several works that used deep learning classifiers to detect patients infected with COVID-19 [9; 10]. [PERSON] et al. [11] found that the characteristics of Computed Tomography (CT) imaging of COVID-19 are different from other types of viral pneumonia (such as Influenza-A viral pneumonia). They used multiple CNN models to classify CT images, calculate the infection probability of COVID-19, and assist in the early screening of patients with COVID-19. They collected a total of 618 CT samples: 219 from 110 patients with COVID-19; 224 CT samples from 224 patients with Influenza-A viral pneumonia; and 175 CT samples from healthy people. Then, they pre-processed the CT images to extract effective pulmonary regions. Then, they used a 3D CNN model based on ResNet18 to segment multiple candidate image cubes. They chose a 3D image classification model to be able to categorize all the image patches. The location attention classification model uses the relative distance from-edge as extra weight for the model. This classification model aims to learn the relative location information of the patch on the pulmonary image. They acquired a total of 11,871 image patches, including 2634 COVID-19, 2661 Influenza-A-viral-pneumonia, and 6576 irrelevant-to-infection. Finally, they used the Noisy or Bayesian function to calculate the infection type (COVID-19, Influenza-A-viral-pneumonia, or no-infection-found) and the total confidence score of the CT case. They only compared the average f1-score for the first two classes, which showed an enhancement of 4.7%, with an overall classification accuracy of 86.7% for all three groups. Because there is no automatic tool to quantify the infection volume for COVID-19 patients clinically, [PERSON] et al. [12] proposed to develop a Deep Learning-based system called \"VB Net\" neural network for automatic segmentation and quantification of COVID-19 infection regions in chest CT [13]. This system also aims to accurately estimate the shapes, volumes, and percentage of the infection region. The \"VB Net\" model is a combination between the V-Net model and the bottleneck model. The V-Net extracts global image features using down-sampling and convolution operations, and the bottleneck model integrates fine-grained image features using up-sampling and convolution operations. Compared with V-Net, the speed of VB-Net is much higher because of the bottleneck structure. The system is trained using 249 COVID-19 patients' data and validated on 300 new COVID-19 patients. To accelerate the delineation of COVID-19 CT images used for training, which is very time-consuming, they proposed a human-in-the-loop (HITL) strategy to generate the training samples iteratively. This method assists radiologists to refine the automatic annotation of each case. To evaluate the performance of the DL (Deep Learning) based system, the Dice similarity coefficient, the differences of volume, and the percentage of infection (POI) are calculated between automatic and manual segmentation results on the validation set. The proposed system gave dice similarity coefficients of 91.6 \(\pm\) 10.0% between automatic and manual segmentation and a mean POI estimation error of 0.3% for the whole lung on the validation dataset. The proposed human in the loop strategy reduces the delineation time to 4 min after three iterations of model updating. The segmentation accuracy of deep learning models was evaluated using the Dice similarity coefficient on the entire 300 validation set. It has improved from 85.1 \(\pm\) 11.4%, to 91.0 \(\pm\) 9.6%, and 91.6 \(\pm\) 10.0% with more training data added. The improved segmentation accuracy greatly reduces human intervention and, thus, significantly reduces the time of annotation and labeling. Many studies confirm that among the characteristics of the patients infected with COVID-19 is that they present abnormalities in their chest X-ray images [14; 15]. Motivated by the need for faster interpretation of radiography images, [PERSON] et al.