Otimizando uma CNN baseada em DenseNet para o diagnóstico de COVID19
Abstract
The main method utilized to diagnose COVID-19 it's the RT-PCR, but this method takes time to generate results, so it is necessary to have methods for a quick and effective diagnosis of the virus. One of these methods that proves to be very efficient is the use of Convolutional Neural Networks, that can diagnose the disease through chest x-ray images or CT scans. Thus, our work evaluates the use of the Tree-structured Parzen Estimator algorithm to optimize and build a CNN model specialized in the diagnosis of COVID-19 based on the DenseNet architecture. Thus the model constructed in this paper, got a promising result with an accuracy of 96% and an AUC of 0,96, showing the effectiveness of the optimization algorithm in the construction of the network.
References
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Costa, G., Paiva, A., Braz Júnior, G., & Ferreira, M. (2021). COVID-19 automatic diagnosis with CT images using the novel Transformer architecture. In Anais do XXI Simpósio Brasileiro de Computação Aplicada à Saúde, (pp. 293-301). Porto Alegre: SBC. doi:10.5753/sbcas.2021.16073
Wang, L., Lin, Z. Q., & Wong, A. (2020). Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images. Scientific Reports, 10(1), 1-12.
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4700-4708).
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
Huang, G., Sun, Y., Liu, Z., Sedra, D., & Weinberger, K. Q. (2016, October). Deep networks with stochastic depth. In European conference on computer vision (pp. 646-661). Springer, Cham.
Bergstra, J., Bardenet, R., Bengio, Y., & Kégl, B. (2011). Algorithms for hyper-parameter optimization. Advances in neural information processing systems, 24.
Jones, D. R. (2001). A taxonomy of global optimization methods based on response surfaces. Journal of global optimization, 21(4), 345-383.
Hutter, F., Hoos, H. H., & Leyton-Brown, K. (2011, January). Sequential model-based optimization for general algorithm configuration. In International conference on learning and intelligent optimization (pp. 507-523). Springer, Berlin, Heidelberg.
Akiba, T., Sano, S., Yanase, T., Ohta, T., & Koyama, M. (2019, July). Optuna: A next-generation hyperparameter optimization framework. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining (pp. 2623-2631).
Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
De Boer, P. T., Kroese, D. P., Mannor, S., & Rubinstein, R. Y. (2005). A tutorial on the cross-entropy method. Annals of operations research, 134(1), 19-67.
O'Shea, Keiron, and Ryan Nash. "An introduction to convolutional neural networks." arXiv preprint arXiv:1511.08458 (2015).
Pavlova, M., Terhljan, N., Chung, A. G., Zhao, A., Surana, S., Aboutalebi, H., ... & Wong, A. (2021). COVID-Net CXR-2: An enhanced deep convolutional neural network design for detection of covid-19 cases from chest x-ray images. arXiv preprint arXiv:2105.06640.
Aslan, M. F., Unlersen, M. F., Sabanci, K., & Durdu, A. (2021). CNN-based transfer learning-BiLSTM network: A novel approach for COVID-19 infection detection. Applied Soft Computing, 98, 106912.
Al-Rakhami, M. S., Islam, M. M., Islam, M. Z., Asraf, A., Sodhro, A. H., & Ding, W. (2021). Diagnosis of COVID-19 from X-rays using combined CNN-RNN architecture with transfer learning. MedRxiv, 2020-08.
Aslan, M. F., Sabanci, K., Durdu, A., & Unlersen, M. F. (2022). COVID-19 diagnosis using state-of-the-art CNN architecture features and Bayesian Optimization. Computers in Biology and Medicine, 105244.
Costa, G., Paiva, A., Braz Júnior, G., & Ferreira, M. (2021). COVID-19 automatic diagnosis with CT images using the novel Transformer architecture. In Anais do XXI Simpósio Brasileiro de Computação Aplicada à Saúde, (pp. 293-301). Porto Alegre: SBC. doi:10.5753/sbcas.2021.16073
Wang, L., Lin, Z. Q., & Wong, A. (2020). Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images. Scientific Reports, 10(1), 1-12.
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4700-4708).
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
Huang, G., Sun, Y., Liu, Z., Sedra, D., & Weinberger, K. Q. (2016, October). Deep networks with stochastic depth. In European conference on computer vision (pp. 646-661). Springer, Cham.
Bergstra, J., Bardenet, R., Bengio, Y., & Kégl, B. (2011). Algorithms for hyper-parameter optimization. Advances in neural information processing systems, 24.
Jones, D. R. (2001). A taxonomy of global optimization methods based on response surfaces. Journal of global optimization, 21(4), 345-383.
Hutter, F., Hoos, H. H., & Leyton-Brown, K. (2011, January). Sequential model-based optimization for general algorithm configuration. In International conference on learning and intelligent optimization (pp. 507-523). Springer, Berlin, Heidelberg.
Akiba, T., Sano, S., Yanase, T., Ohta, T., & Koyama, M. (2019, July). Optuna: A next-generation hyperparameter optimization framework. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining (pp. 2623-2631).
Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
De Boer, P. T., Kroese, D. P., Mannor, S., & Rubinstein, R. Y. (2005). A tutorial on the cross-entropy method. Annals of operations research, 134(1), 19-67.
Published
2022-06-07
How to Cite
COSTA, Gabriel de Jesus S.; OLIVEIRA, Marcus Vinicius; FREITAS, Mario; BESSA, Matheus de Lima; BRAZ JUNIOR, Geraldo; ALMEIDA, João Dallyson S. de; PAIVA, Anselmo C..
Otimizando uma CNN baseada em DenseNet para o diagnóstico de COVID19. In: BRAZILIAN SYMPOSIUM ON COMPUTING APPLIED TO HEALTH (SBCAS), 22. , 2022, Teresina.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 289-298.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2022.222666.
