Classification of CT images in COVID-19 and Non-COVID-19 using CNN to extract features and multiple classifiers
Abstract
The COVID-19 is a respiratory disease that has infected more than 12.3 million people worldwide and is responsible for more than 556,300 deaths. The early diagnosis of COVID-19 is essential for the cure and control of the disease. Computed tomography (CT) showed efficient results in the evaluation of patients with suspected COVID-19 infection. CT analysis requires the effort of a specialist, which can lead to diagnostic errors. The use of computer-aided diagnostic systems can minimize the problems generated by CT analysis by specialists. This article presents a methodology for diagnosing COVID-19 using CNN to extract features and multiple classifiers from CT images. The methodology showed an accuracy of 99.79%, recall of 99.79%, accuracy of 99.80%, F-score of 0.997, AUC of 0.997 and kappa index of 0.995. The results obtained show that the proposed methodology can be used as an aid to diagnosis.
References
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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.
Published
2020-09-10
How to Cite
CARVALHO, Edelson; CARVALHO, Edson.
Classification of CT images in COVID-19 and Non-COVID-19 using CNN to extract features and multiple classifiers. In: REGIONAL SCHOOL ON COMPUTING OF CEARÁ, MARANHÃO, AND 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.
