# COVID-19 Diagnosis in Chest X-rays Using Deep Learning and Majority Voting
## 1 Introduction
### Related Works
Nevertheless, such a heterogeneous dataset may introduce some bias in the results. Moreover, we will show in Section 3.3 that we can reach a higher accuracy without resorting to supplementary synthetic images. To differentiate COVID-19 cases from other pneumonia cases, [PERSON] et al. [29] proposed to build open source and open access chest X-rays datasets and presented an accurate Convolutional Neural Network framework [30]. They also used an updated version of the COVIDx dataset recently made public by the authors of the COVID-Net [16] previously described. It consists of a total of 5941 chest radiography images from 2839 patients with four classes. There are 68 COVID-19 radiographs from 45 COVID-19 patients. There were a total of 1203 patients with negative pneumonia: normal class, 931 patients with bacterial pneumonia, and 660 patients with non-COVID-19 viral pneumonia cases. To solve the imbalanced classification problem caused by the small number of COVID-19 images, they proposed to use data augmentation techniques. The transformations used included vertical flips of the training images, random rotation of the images (maximum rotation angle was 15 degrees), and lighting conditions. They chose to employ the ResNet50 model for the classification task because it provides a good trade-off between performance and number of parameters, has proved faster training, and it is possible to produce images with different sizes than the training images. The ResNet50 model is pre-trained on the ImageNet dataset [31] and fine-tuned with the COVIDx dataset. The input images are resized to \(128\times 128\times 3\), \(224\times 224\times 3\), and \(299\times 299\times 3\) pixels and are employed in different training stages. For training a high-performance network with very few epochs, they used the transfer learning techniques introduced in Fastai [32]. This technique replaces the head of the trained model by another containing a sequence of Adaptive average/max pooling, batch normalization, drop out, and linear layers. The resultant network is called COVID-ResNet. This approach achieved a state-of-the-art accuracy of 96.23% on the COVIDx dataset with only 41 epochs and 25.6 M parameters. To study the application of the COVID-19 detection application based on deep learning models from the chest X-ray images, [PERSON] et al. [33] started by preparing a dataset of 5000 chest X-rs from the publicly available datasets. Then, they used Transfer learning on a subset of 2000 radiograms to train 4 CNN models, including ResNet18, ResNet50, SqueezeNet, and DenseNet-121, to identify COVID-19 disease in the analyzed chest X-ray images. Finally, they evaluated these models on the 3000 images. Most of these networks achieved a sensitivity rate of 98% (\(\pm\)3%) and a specificity rate of around 90%. Table 1 summarizes the datasets, algorithms, and results of the most similar related works on COVID-19 detection, compared to the present paper. In this table, row 1 will refer to the work introduced by [PERSON] [11]; row 2 will refer to the work introduced by Reference [12], row 3 will refer to the work introduced by [PERSON] [16], row 4 will refer to the work introduced by [PERSON] [29], row 5 will refer to the work introduced by Reference [33], and the last row will describe the output generated by our method. The remainder of the paper is organized as follows. Section 2 describes the methods and the materials used in this study: the characteristics of the COVID-19 dataset, the data collection and the cleaning processes, and, finally, the 16 deep learning models selected. Section 3 discusses the main results. Section 4 concludes the paper and outlines future works.