Convolutional Neural Networks Evaluation for COVID-19 Classification on Chest Radiographs

Abstract

Early identification of patients with COVID-19 is essential to enable adequate treatment and to reduce the burden on the health system. The gold standard for COVID-19 detection is the use of RT-PCR tests. However, due to the high demand for tests, these can take days or even weeks in some regions of Brazil. Thus, an alternative for the detection of COVID-19 is the analysis of Chest X-rays (CXR). This paper proposes the evaluation of convolutional neural networks to identify pneumonia due to COVID-19 in CXR. The proposed methodology consists of an evaluation of six convolutional architectures pre-trained with the ImageNet dataset: InceptionResNetV2, InceptionV3, MovileNetV2, ResNet50, VGG16, and Xception. The obtained results for our methodology demonstrate that the Xception architecture presented a superior performance in the classification of CXR, with an Accuracy of 85.64%, Sensitivity of 85.71%, Specificity of 85.65%, F1-score of 85.49%, and an AUC of 0.9648.