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 detecting COVID-19 is the analysis of Digital Chest X-rays (XR). Changes due to COVID-19 can be detected in XR, even in asymptomatic patients. In this context, models based on deep learning have great potential to be used as support systems for diagnosis or as screening tools. In this paper, we propose the evaluation of convolutional neural networks to identify pneumonia due to COVID-19 in XR. The proposed methodology consists of a preprocessing step of the XR, data augmentation, and classification by the convolutional architectures DenseNet121, InceptionResNetV2, InceptionV3, MovileNetV2, ResNet50, and VGG16 pre-trained with the ImageNet dataset. The obtained results for our methodology demonstrate that the VGG16 architecture presented a superior performance in the classification of XR, with an Accuracy of \(85.11\%\), Sensitivity of \(85.25\%\), Specificity of \(85.16\%\), F1-score of \(85.03\%\), and an AUC of 0.9758.
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Acknowledgements
We thank the anonymous reviewers for their valuable suggestions. The authors would like to thank the Coordination for the Improvement of Higher Education Personnel - CAPES (Financial Code 001), the National Council for Scientific and Technological Development - CNPq (Grant numbers 309537/2020-7), the Research Support Foundation of the State of Rio Grande do Sul - FAPERGS (Grant numbers 08/2020 PPSUS 21/2551-0000118-6), and NVIDIA GPU Grant Program for your support in this work.
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Zeiser, F.A., Costa, C.A.d., Ramos, G.d.O., Bohn, H., Santos, I., Righi, R.d.R. (2021). Evaluation of Convolutional Neural Networks for COVID-19 Classification on Chest X-Rays. In: Britto, A., Valdivia Delgado, K. (eds) Intelligent Systems. BRACIS 2021. Lecture Notes in Computer Science(), vol 13074. Springer, Cham. https://doi.org/10.1007/978-3-030-91699-2_9
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