# COVID-19 Diagnosis in Chest X-rays Using Deep Learning and Majority Voting
## 1 Introduction
### COVID-19 Diagnosis in Chest X-rays Images  
Several approaches have been used to screen for the virus that causes COVID-19. We opted for screening by analyzing the medical image. Medical image analysis includes image acquisition, detection, segmentation, recognition, classification, diagnosis, and follow-up.  
We focused on virus screening through image recognition. This recognition is undertaken through classification among various viruses. We profited from the progress conducted on deep learning [1; 2; 3; 4; 5; 6; 7]. Deep Learning is a sub-field of machine learning dealing with algorithms in tune with the structure and function of the brain-known as artificial neural networks. Deep learning builds features automatically based on training data. It combines feature extraction and classification. For feature extraction and image classification, the Convolutional Neural Network (CNN) turned out to be the neural network offering the most promising avenue for deep learning. This avenue branches off in several structures of the network, such as AlexNet, VGGNet, ResNet, Inception, and EfficientNet. One structure may be implemented by more than one algorithm. The main interesting quality of deep learning is that it can be composed and extended in various ways to solve a variety of more complex tasks. By using this quality, we contributed, among others, to the adaptation of a list of deep learning algorithms to our specific application of COVID-19 detection.  
In this paper, the main challenges are the identification of Coronavirus cases in blurred X-ray images and the differentiation of these cases of COVID-19 from other pneumonia cases, like MERS and SARS [8], bearing in mind that they have a high degree of similarity. Second, the COVID-19 virus does not have a fixed shape, circular, for example, inside the human tissue. Third, the decision resulting from the detection process is very delicate and can put human beings at risk. False-Negative means, in our situation, a person infected by the virus, who is declared by our process safe and sound, while the patient may be at risk of death, and even worse: before dying, he/she may infect thousands of people. Thus, the rate of False-Negatives should be literally zero.  
The originality of our work lies in the following aspects: First, we composed a dataset enabling deep training within COVID-19 related images, and we improve the performance of the dataset by removing duplicate images giving our deep neural network models additional opportunities to learn unbiasedly the different patterns existing in the data. Second, we considered and trained 16 state-of-the-art deep learning models to classify X-ray images into three classes, normal, pneumonia, and COVID-19. Third, we selected the five best classifiers and combined them using five voting approaches (hard/soft majority, weighted voting, Support Vector Machine, and Random Forest) improve the classification accuracy. We found that the ensemble classification to with the hard voting approach achieves the best accuracy up to 99.314%, by leveraging the combination of the classifiers. To the best of our knowledge, this is the first work that evaluates 16 classifiers and 5 voting approaches for the classification of COVID-19 from X-rays.