Análise da Segmentação e Extração de Características na Detecção de COVID-19 em imagens de Raio-x de Tórax
Resumo
O COVID-19 afeta principalmente os pulmões, causando falta de ar, tosse e até falência de múltiplos órgãos, deixando as pessoas gravemente doentes. A radiografia de tórax se torna necessária para testes e para avaliar o pulmão e o progresso do vírus quanto aos seus efeitos. Este trabalho apresenta a avaliação dos descritores Dense Net201, VGG16, RESNET50 e Xception, e os classificadores Multi-layer Perceptron (MLP) e Random Forest (RF), com a utilização da base COVID-19 chest x-ray database para o diagnóstico do COVID-19. Para avaliar a segmentação foi utilizada a base Tuberculosis (TB) Chest X-ray Database. Os testes foram realizados em um conjunto de imagens segmentadas contendo 6012 imagens de infecções pulmonares, 3616 de COVID-19 e 10192 sem achados. Os cenários avaliados foram (Covid x Normal), (Covid x Opacidade Pulmonar), (Covid x Opacidade Pulmonar x Normal). Os melhores resultados foram alcançados com o descritor DenseNet201 e o classificador MLP no cenário (Covid x Normal), com Acurácia e Kappa de 0,99.Referências
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Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1251–1258.
da Nobrega, R. V. M. et al. (2018). Lung nodule classication via deep transfer learning in ct lung images. In 2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS), pages 244–249.
He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep residual learning for image recognition. In Conference on Computer Vision and Pattern Recognition, volume 2016December, pages 770–778. IEEE Computer Society.
Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4700–4708.
Narin, A., Kaya, C., and Pamuk, Z. (2020). Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks. arXiv preprint arXiv:2003.10849.
Nicolas, P. R. (2015). Scala for machine learning. Packt Publishing Ltd.
Rahman, T., Khandakar, A., Kadir, M. A., Islam, K. R., Islam, K. F., Mazhar, R., Hamid, T., Islam, M. T., Kashem, S., Mahbub, Z. B., et al. (2020). Reliable tuberculosis detection using chest x-ray with deep learning, segmentation and visualization. IEEE Access, 8:191586–191601.
Simonyan, K. and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
Suthaharan, S. (2016). Machine learning models and algorithms for big data classication. Integr. Ser. Inf. Syst, 36:1–12.
T. R. Muhammad, E. H. Chowdhury, A. K. R. M. M. A. K. Z. B. M. e. a. (2020). Covid-19 chest x-ray database. online. Available: https://www.kaggle.com/tawsifurrahman/covid19-radiography-database.
Tang, H., Kim, D. R., and Xie, X. (2018). Automated pulmonary nodule detection using 3d deep convolutional neural networks. In 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pages 523–526.
Teixeira, L. O., Pereira, R. M., Bertolini, D., Oliveira, L. S., Nanni, L., Cavalcanti, G. D., and Costa, Y. M. (2020). Impact of lung segmentation on the diagnosis and explanation of covid-19 in chest x-ray images. arXiv preprint arXiv:2009.09780.
Varela-Santos, S. and Melin, P. (2021). A new approach for classifying coronavirus covid-19 based on its manifestation on chest x-rays using texture features and neural networks. Information sciences, 545:403–414.
Publicado
23/11/2021
Como Citar
ALMONDES, Camila Catiely de Sá; ARAÚJO, Flávio Henrique Duarte de.
Análise da Segmentação e Extração de Características na Detecção de COVID-19 em imagens de Raio-x de Tórax. In: ENCONTRO UNIFICADO DE COMPUTAÇÃO DO PIAUÍ (ENUCOMPI), 14. , 2021, Picos.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
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p. 1-8.
DOI: https://doi.org/10.5753/enucompi.2021.17747.
