xRayAID Detecting Pneumonia Using Artificial Intelligence
Resumo
Pneumonia is a type of acute respiratory infection that impacts people's lives in several ways, demanding an accurate and fast diagnosis. High death rates, massive socioeconomic impacts, and a significant gap between the number of available doctors based on its geographic location are some of the problems surrounding this topic. The xRayAID is a tool that uses machine learning to assist doctors in diagnosis of pneumonia on frontal chest radiographs. That was done by using a modified DenseNet-121 neural network architecture trained on the Radiological Society of North America (RSNA) public dataset. The results showed that this tool is able to help doctors to identify pneumonia scenarios, achieving a validation accuracy of 87.9%.
Referências
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DATASUS (2020 (accessed March 7, 2020)). Tecnologia da Informação a Serviço do SUS. http://tabnet.datasus.gov.br/cgi/tabcgi.exe?sih/cnv/nruf.def.
Fundação Oswaldo Cruz (2013 (accessed March 1, 2020)). Pneumonia. https://agencia.fiocruz.br/pneumonias.
Guzzetta, P., Toews, G. B., Robertson, K. J., and Pierce, A. K. (1983). Rapid diagnosis of community-acquired bacterial pneumonia. American Review of Respiratory Disease, 128(3):461–464.
Han, Y., Chen, C., Tewk, A. H., Ding, Y., and Peng, Y. (2021). Pneumonia detection on chest x-ray using radiomic features and contrastive learning. arXiv preprint arXiv:2101.04269.
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.
Organization, W. H. et al. (2001). Standardization of interpretation of chest radiographs for the diagnosis of pneumonia in children. Technical report, World Health Organization.
Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., Langlotz, C., Shpanskaya, K., et al. (2017). Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv preprint arXiv:1711.05225.
RSNA (2018 (accessed March 25, 2020)). RSNA Pneumonia Detection Challenge. https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/overview.
Scheffer, M., Biancarelli, A., and Cassenote, A. (2018). Demograa médica no brasil 2015 [internet]. São Paulo: Departamento de Medicina Preventiva, Faculdade de Medicina da USP, page 284.
Sethy, P. K. and Behera, S. K. (2020). Detection of coronavirus disease (covid-19) based on deep features. Preprints, 2020030300:2020.
Stephen, O., Sain, M., Maduh, U. J., and Jeong, D.-U. (2019). An efcient deep learning approach to pneumonia classication in healthcare. Journal of healthcare engineering, 2019.
Tsai, E. B., Simpson, S., Lungren, M., Hershman, M., Roshkovan, L., Colak, E., Erickson, B. J., Shih, G., Stein, A., Kalpathy-Cramer, J., et al. (2021). The rsna international covid-19 open annotated radiology database (ricord). Radiology, page 203957.
DATASUS (2020 (accessed March 7, 2020)). Tecnologia da Informação a Serviço do SUS. http://tabnet.datasus.gov.br/cgi/tabcgi.exe?sih/cnv/nruf.def.
Fundação Oswaldo Cruz (2013 (accessed March 1, 2020)). Pneumonia. https://agencia.fiocruz.br/pneumonias.
Guzzetta, P., Toews, G. B., Robertson, K. J., and Pierce, A. K. (1983). Rapid diagnosis of community-acquired bacterial pneumonia. American Review of Respiratory Disease, 128(3):461–464.
Han, Y., Chen, C., Tewk, A. H., Ding, Y., and Peng, Y. (2021). Pneumonia detection on chest x-ray using radiomic features and contrastive learning. arXiv preprint arXiv:2101.04269.
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.
Organization, W. H. et al. (2001). Standardization of interpretation of chest radiographs for the diagnosis of pneumonia in children. Technical report, World Health Organization.
Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., Langlotz, C., Shpanskaya, K., et al. (2017). Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv preprint arXiv:1711.05225.
RSNA (2018 (accessed March 25, 2020)). RSNA Pneumonia Detection Challenge. https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/overview.
Scheffer, M., Biancarelli, A., and Cassenote, A. (2018). Demograa médica no brasil 2015 [internet]. São Paulo: Departamento de Medicina Preventiva, Faculdade de Medicina da USP, page 284.
Sethy, P. K. and Behera, S. K. (2020). Detection of coronavirus disease (covid-19) based on deep features. Preprints, 2020030300:2020.
Stephen, O., Sain, M., Maduh, U. J., and Jeong, D.-U. (2019). An efcient deep learning approach to pneumonia classication in healthcare. Journal of healthcare engineering, 2019.
Tsai, E. B., Simpson, S., Lungren, M., Hershman, M., Roshkovan, L., Colak, E., Erickson, B. J., Shih, G., Stein, A., Kalpathy-Cramer, J., et al. (2021). The rsna international covid-19 open annotated radiology database (ricord). Radiology, page 203957.
Publicado
15/06/2021
Como Citar
TREVISAN, Vinicius; RODRIGUES, Daniele U. M.; REZENDE, Edmar R. S..
xRayAID Detecting Pneumonia Using Artificial Intelligence. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 21. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 1-12.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2021.16048.
