Classification of CT images in COVID-19 and Non-COVID-19 using CNN to extract features and multiple classifiers
Resumo
O COVID-19 é uma doença respiratória que já infectou mais de 12.3 milhões de pessoas em todo o mundo e é responsável por mais de 556.300 mortes. O diagnóstico precoce do COVID-19 é essencial para a cura e controle da doença. A tomografia computadorizada (TC) apresentou resultados eficientes na avaliação de pacientes com suspeita de infecção por COVID-19. A análise da TC requer o esforço de um especialista, o que pode levar a erros de diagnóstico. O uso de sistemas de diagnóstico auxiliado por computador pode minimizar os problemas gerados pela análise de TCs por especialistas. Este artigo apresenta uma metodologia para diagnosticar a COVID-19 usando CNN para extração de características e múltiplos classificadores em imagens de TC. A metodologia apresentou uma acurácia de 99,79%, recall de 99,79%, precisão de 99,80%, F-score de 0,997, AUC de 0,997 e índice kappa de 0,995. Os resultados obtidos mostram que a metodologia proposta pode ser utilizada como um sistema de auxílio ao diagnóstico.
Referências
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Breiman, L. (2001). Random forests. Machine Learning, 45(1):5–32.
Chen, T. and Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16, page 785–794, New York, NY, USA. Association for Computing Machinery.
Gorbalenya, A. E., Baker, S. C., Baric, R. S., de Groot, R. J., Drosten, C., Gulyaeva, A. A., Haagmans, B. L., Lauber, C., Leontovich, A. M., Neuman, B. W., Penzar, D., Perlman, S., Poon, L. L., Samborskiy, D., Sidorov, I. A., Sola, I., and Ziebuhr, J. (2020). Severe acute respiratory syndrome-related coronavirus: The species and its viruses – a statement of the coronavirus study group. bioRxiv.
Gozes, O., Frid-Adar, M., Greenspan, H., Browning, P. D., Zhang, H., Ji, W., Bernheim, A., and Siegel, E. (2020). Rapid ai development cycle for the coronavirus (covid19) pandemic: Initial results for automated detection patient monitoring using deep learning ct image analysis.
He, X., Yang, X., Zhang, S., Zhão, J., Zhang, Y., Xing, E., and Xie, P. (2020). Sampleefficient deep learning for covid-19 diagnosis based on ct scans. medRxiv.
Liu, X., Hou, F., Qin, H., and Hão, A. (2018). Multi-view multi-scale cnns for lung nodule type classification from ct images. Pattern Recognition, 77:262 – 275.
Masci, J., Meier, U., Cires¸an, D., and Schmidhuber, J. (2011). Stacked convolutional auto-encoders for hierarchical feature extraction. In Honkela, T., Duch, W., Girolami, M., and Kaski, S., editors, Artificial Neural Networks and Machine Learning – ICANN 2011, pages 52–59, Berlin, Heidelberg. Springer Berlin Heidelberg.
Ozkaya, U., Ozturk, S., and Barstugan, M. (2020). Coronavirus (covid-19) classification using deep features fusion and ranking technique.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830.
Ren, X., Guo, H., Li, S.,Wang, S., and Li, J. (2017). A novel image classification method with cnn-xgboost model. pages 378–390.
Ripley, B. D. (1996). Pattern Recognition and Neural Networks. Cambridge University Press.
Singh, D., Kumar, V., Vaishali, and Kaur, M. (2020). Classification of covid-19 patients from chest ct images using multi-objective differential evolution-based convolutional neural networks. European journal of clinical microbiology infectious diseases : official publication of the European Society of Clinical Microbiology.
Smith, I., Lister, R., Ray, M., and Hawson, G. (2001). Naive bayesian prediction of bleeding after heart by-pass surgery. In The Seventh Australian and New Zealand Intelligent Information Systems Conference, 2001, pages 317–321.
Soares, E., Angelov, P., Biaso, S., Higa Froes, M., and Kanda Abe, D. (2020). Sars-cov-2 ct-scan dataset: A large dataset of real patients ct scans for sars-cov-2 identification. medRxiv.
van Ginneken, B., Setio, A. A. A., Jacobs, C., and Ciompi, F. (2015). Off-the-shelf convolutional neural network features for pulmonary nodule detection in computed tomography scans. In 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), pages 286–289.
WHO, W. H. O. (2020). Coronavirus disease (covid-19) outbreak situation.
Yonghong Li and Jain, A. K. (1998). Classification of text documents. In Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170), volume 2, pages 1295–1297 vol.2.
Zhu, R., Zhang, R., and Xue, D. (2015). Lesion detection of endoscopy images based on convolutional neural network features. In 2015 8th International Congress on Image and Signal Processing (CISP), pages 372–376.
Publicado
10/09/2020
Como Citar
CARVALHO, Edelson; CARVALHO, Edson.
Classification of CT images in COVID-19 and Non-COVID-19 using CNN to extract features and multiple classifiers. In: ESCOLA REGIONAL DE COMPUTAÇÃO DO CEARÁ, MARANHÃO E PIAUÍ (ERCEMAPI), 8. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 260-267.
DOI: https://doi.org/10.5753/ercemapi.2020.11493.
