Evaluation of Convolutional Neural Networks for COVID-19 Classification on Chest X-Rays

• URL: http://arxiv.org/abs/2109.02415v1
• Date: Mon, 6 Sep 2021 12:57:55 GMT
• Title: Evaluation of Convolutional Neural Networks for COVID-19 Classification on Chest X-Rays
• Authors: Felipe Andr\'e Zeiser, Cristiano Andr\'e da Costa, Gabriel de Oliveira Ramos, Henrique Bohn, Ismael Santos, Rodrigo da Rosa Righi
• Abstract summary: We propose the evaluation of convolutional neural networks to identify pneumonia due to COVID-19 in Digital Chest X-rays. The proposed methodology consists of a preprocessing step of the XR, data augmentation, and classification by the convolutional architectures DenseNet121, InResceptionNetV2, InceptionV3, MovileNetV2, ResNet50, and VGG16. The obtained results demonstrate that the VGG16 architecture presented a superior performance in the classification of XR.
• Score: 7.509537244787675
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 demonstrate that the VGG16 architecture obtained superior performance in the classification of XR for the evaluation metrics using the methodology proposed in this article. 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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