ABSTRACT
Due to the urgency of the COVID pandemic, it is necessary to develop new and quick methods to detect the infection and stop the spread of the disease. In this work we compare a simple Deep Learning (DL) model with an ensemble model in the task of COVID detection in X-Ray images. For the simple model, we have used only frontal DX X-Ray images while, for the ensemble model, we have used frontal DX and CR X-Ray images, as well as lateral DX and CR X-Ray images. In the ensemble model, the features of the four images are combined to make a final prediction and, since not every patient possess all types of images, the model is also robust against missing information, which is crucial in these types of models. Although the dataset used is very noisy, the presented system has shown the desired robustness and offers relevant results, showing that ensemble models can generalize better over the data, which leads to a higher accuracy. Finally, we share our conclusions and discuss future work where we want to try using a similar methodology. © 2021, Springer Nature Switzerland AG.