Por um modelo de classificação usando CNN e Wavelets aplicados a imagens de TC COVID-19
DOI:
https://doi.org/10.33448/rsd-v11i5.27919Palavras-chave:
Redes Neurais Convolucionais; COVID-19; Wavelets; Imagens de TC; WCNN-COVID.Resumo
No final de 2019, um novo tipo de coronavírus surgiu na China e recebeu o nome de SARS-CoV-2. Primeiro impactou o país onde surgiu e depois se espalhou pelo mundo. O SARS-CoV-2 é a causa da doença COVID-19 que deixa impressões características nas imagens de TC de tórax dos pacientes infectados. Neste artigo, propomos um modelo de classificação, baseado em CNN e transformada wavelet, para classificar imagens de pacientes COVID-19. Ele foi denominado WCNN-COVID. O modelo foi aplicado e testado em repositórios de imagens de TC abertos e privados. Foram processadas 25534 imagens de 200 pacientes. A matriz de confusão foi gerada pelo cálculo de Acurácia (ACC), Sensibilidade (Sen) e Especificidade (Sp). A curva Receiver Operating Characteristic (ROC) e a Área Sob a Curva (AUCs) também foram plotadas e usadas para avaliação. Os resultados das métricas foram ACC = 0,9950, Sen = 99,16% e Sp = 99,89%.
Referências
Abbas, A., Abdelsamea, M., & Gaber, M. (2020, 4). Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network. https://doi.org/10.1101/2020.03.30.20047456
Aggarwal, C. C., & others. (2018). Neural networks and deep learning. Springer https://doi.org/10.1007/978-3-319-94463-0
Balas, V. E., Roy, S. S., Sharma, D., & Samui, P. (2019). Handbook of deep learning applications (Vol. 136). Springer. https://doi.org/10.1007/978-3-030-11479-4
Barstugan, M., Ozkaya, U., & Ozturk, S. (2020). Coronavirus (covid-19) classification using ct images by machine learning methods. arXiv preprint arXiv:2003.09424.
Bassi, P. R., & Attux, R. (2020). A Deep Convolutional Neural Network for COVID-19 Detection Using Chest X-Rays. arXiv preprint arXiv:2005.01578.
Chen(a), H., Guo, S., Hao, Y., Fang, Y., Fang, Z., Wu, W., & Li, S. (2021). Auxiliary Diagnosis for COVID-19 with Deep Transfer Learning. Journal of Digital Imaging, 1–11.
Chen, J., Wu, L., Zhang, J., Zhang, L., Gong, D., Zhao, Y., et al. (2020). Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography: a prospective study. MedRxiv.
Chollet, F. (2016). Building powerful image classification models using very little data. Keras Blog. Retrieved from https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html
Cui, J., Li, F., & Shi, Z.-L. (2019). Origin and evolution of pathogenic coronaviruses. Nature Reviews Microbiology, 17, 181–192. https://doi.org/10.1038/s41579-018-0118-9
da Costa Junior, C. A., & Patrocinio, A. C. (2019). Performance Evaluation of Denoising Techniques Applied to Mammograms of Dense Breasts. XXVI Brazilian Congress on Biomedical Engineering, (pp. 369–374).
Dai, W.-c., Zhang, H.-w., Yu, J., Xu, H.-j., Chen, H., Luo, S.-p., et al., (2020). CT imaging and differential diagnosis of COVID-19. Canadian Association of Radiologists Journal, 71, 195–200. https://doi.org/10.1177/0846537120913033
dos S Ribeiro, C., van Roode, M. Y., Haringhuizen, G. B., Koopmans, M. P., Claassen, E., & van de Burgwal, L. H. (2018). How ownership rights over microorganisms affect infectious disease control and innovation: A root-cause analysis of barriers to data sharing as experienced by key stakeholders. PLoS One, 13, e0195885. https://doi.org/10.1371/journal.pone.0195885
Guo, T., Seyed Mousavi, H., Huu Vu, T., & Monga, V. (2017). Deep wavelet prediction for image super-resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, (pp. 104–113).
He, K., & Sun, J. (2015). Convolutional neural networks at constrained time cost. Proceedings of the IEEE conference on computer vision and pattern recognition, (pp. 5353–5360).
Ishitaki, T., Oda, T., & Barolli, L. (2016). A neural network based user identification for Tor networks: Data analysis using Friedman test. 2016 30th International Conference on Advanced Information Networking and Applications Workshops (WAINA), (pp. 7–13). https://doi.org/10.1109/waina.2016.143
Jansen, M. (2012). Noise reduction by wavelet thresholding (Vol. 161). Springer Science & Business Media. https://doi.org/10.1007/978-1-4613-0145-5
Khatami, A., Khosravi, A., Nguyen, T., Lim, C. P., & Nahavandi, S. (2017). Medical image analysis using wavelet transform and deep belief networks. Expert Systems with Applications, 86, 190–198. https://doi.org/10.1016/j.eswa.2017.05.073
Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, (pp. 1097–1105).
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521, 436–444. https://doi.org/10.1038/nature14539
Maranhão, A. (2020). COVID-19 CT: scans20 CT scans and expert segmentations of patients with COVID-19. Retrieved 02 02, 2021, from Kaggle: https://www.kaggle.com/andrewmvd/covid19-ct-scans
Martin, D. R., Hanson, J. A., Gullapalli, R. R., Schultz, F. A., Sethi, A., & Clark, D. P. (2020). A deep learning convolutional neural network can recognize common patterns of injury in gastric pathology. Archives of pathology & laboratory medicine, 144, 370–378. https://doi.org/10.5858/arpa.2019-0004-OA~
Merry, R. J. (2005). Wavelet theory and applications: a literature study. DCT rapporten, 2005.
MosMedData. (2020). MosMedData: COVID19_1000 Dataset:Chest CT Scans with COVID-19. Retrieved from https://mosmed.ai/en/
Narin, A., Kaya, C., & Pamuk, Z. (2020). Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks. arXiv preprint arXiv:2003.10849.
Ozkaya, U., Ozturk, S., & Barstugan, M. (2020). Coronavirus (COVID-19) Classification using Deep Features Fusion and Ranking Technique. arXiv preprint arXiv:2004.03698.
Ozturk, S., Ozkaya, U., & Barstugan, M. (2020). Classification of coronavirus images using shrunken features. medRxiv. https://doi.org/10.1101/2020.04.03.20048868
PINHEIRO, J. I., CUNHA, S. B., CARVAJAL, S. R., & GOMES, G. C. (2009). Estatı́stica Básica: A arte de trabalhar com dados. Rio de Janeiro–RJ. Estatı́stica Básica: A arte de trabalhar com dados. Rio de Janeiro–RJ. Editora Elsevier.
Ponti, M. A., & da Costa, G. B. (2018). Como funciona o deep learning. arXiv preprint arXiv:1806.07908.
Rafael, C. (2006). Gonzalez, and Richard E. Woods. Digital image processing.
Ravı̀, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., & Yang, G.-Z. (2016). Deep learning for health informatics. IEEE journal of biomedical and health informatics, 21, 4–21. https://doi.org/10.1109/JBHI.2016.2636665
Ribeiro, C. d., Koopmans, M. P., & Haringhuizen, G. B. (2018). Threats to timely sharing of pathogen sequence data. Science, 362, 404–406. https://doi.org/10.1126/science.aau5229
Ruuska, S., Hämäläinen, W., Kajava, S., Mughal, M., Matilainen, P., & Mononen, J. (2018). Evaluation of the confusion matrix method in the validation of an automated system for measuring feeding behaviour of cattle. Behavioural processes, 148, 56–62. https://doi.org/10.1016/j.beproc.2018.01.004
Sethy, P. K., & Behera, S. K. (2020). Detection of coronavirus disease (covid-19) based on deep features. Preprints, 2020030300, 2020. https://doi.org//10.20944/preprints202003.0300.v1
Sherry, S. T., Ward, M.-H., Kholodov, M., Baker, J., Phan, L., Smigielski, E. M., & Sirotkin, K. (2001). dbSNP: the NCBI database of genetic variation. Nucleic acids research, 29, 308–311. https://doi.org/10.1093/nar/29.1.308
Shirazi, A. Z., Chabok, S. J., & Mohammadi, Z. (2018). A novel and reliable computational intelligence system for breast cancer detection. Medical & biological engineering & computing, 56, 721–732. https://doi.org/10.1007/s11517-017-1721-z
Simon, J. H., Claassen, E., Correa, C. E., & Osterhaus, A. D. (2005). Managing severe acute respiratory syndrome (SARS) intellectual property rights: the possible role of patent pooling. Bulletin of the World Health Organization, 83, 707–710.
Skansi, S. (2018). Introduction to Deep Learning: from logical calculus to artificial intelligence. Springer. doi:https://doi.org/10.1007/978-3-319-73004-2
Summers, R. (2020). NIH Clinical Center:dataset of 32,000 CT images. Retrieved from https://www.nih.gov/news-events/news-releases/nih-clinical-center-releases-dataset-32000-ct-images
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., & Rabinovich, A. (2015). Going deeper with convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition, (pp. 1–9). https://doi.org/10.1109/CVPR.2015.7298594
Wang(d), G., Liu, X., Li, C., Xu, Z., Ruan, J., Zhu, H., & Zhang, S. (2020). A Noise-robust Framework for Automatic Segmentation of COVID-19 Pneumonia Lesions from CT Images_. IEEE Transactions on Medical Imaging. https://doi.org/10.1109/TMI.2020.3000314
Wang, L., & Wong, A. (2020). COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images. arXiv preprint arXiv:2003.09871.
Wang, X., Deng, X., Fu, Q., Zhou, Q., Feng, J., Ma, H., & Zheng, C. (2020). A Weakly-supervised Framework for COVID-19 Classification and Lesion Localization from Chest CT. IEEE Transactions on Medical Imaging. https://doi.org/10.1109/tmi.2020.2995965
Wani, M. A., Bhat, F. A., Afzal, S., & Khan, A. I. (2020). Advances in deep learning (Vol. 57). Springer. https://doi.org/10.1007/978-981-13-6794-6
Weiss, S. R., & Leibowitz, J. L. (2011). Coronavirus pathogenesis. In Advances in virus research (Vol. 81, pp. 85–164). Elsevier. https://doi.org/10.1016/B978-0-12-385885-6.00009-2
Williams, T., & Li, R. (2016). Advanced image classification using wavelets and convolutional neural networks. 2016 15th IEEE international conference on machine learning and applications (ICMLA), (pp. 233–239). https://doi.org/10.1109/icmla.2016.0046
Wu, J. (2013). Institute of Genomics, Chinese Academy of Science, China National Center for Bioinformation & National Genomics Data Center. Institute of Genomics, Chinese Academy of Science, China National Center for Bioinformation & National Genomics Data Center. China. Retrieved from https://bigd.big.ac.cn/ncov/?lang=en
Wu, X., Hui, H., Niu, M., Li, L., Wang, L., He, B., et al. (2020). Deep learning-based multi-view fusion model for screening 2019 novel coronavirus pneumonia: a multicentre study. European Journal of Radiology, 109041.
Yang, S., Jiang, L., Cao, Z., Wang, L., Cao, J., Feng, R., & Shan, F. (2020). Deep learning for detecting corona virus disease 2019 (COVID-19) on high-resolution computed tomography: a pilot study. Annals of Translational Medicine, 8. https://doi.org/10.21037/atm.2020.03.132
Yang, W., Cao, Q., Qin, L., Wang, X., Cheng, Z., Pan, A., & others. (2020). Clinical characteristics and imaging manifestations of the 2019 novel coronavirus disease (COVID-19): A multi-center study in Wenzhou city, Zhejiang, China. Journal of Infection. https://doi.org/10.1016/j.jinf.2020.02.016
Zeiler, M. D., & Fergus, R. (2014). Visualizing and understanding convolutional networks. European conference on computer vision, (pp. 818–833). https://doi.org/10.1007/978-3-319-10590-1_53
Zhang, J., Xie, Y., Li, Y., Shen, C., & Xia, Y. (2020). Covid-19 screening on chest x-ray images using deep learning based anomaly detection. arXiv preprint arXiv:2003.12338.
Zhu, N., Zhang, D., Wang, W., & others. (n.d.). China Novel Coronavirus Investigating and Research Team. A novel coronavirus from patients with pneumonia in China, 2019 [published January 24, 2020]. N Engl J Med. doi:https://doi.org/10.1056/NEJMoa2001017
Zimmerman, D. W., & Zumbo, B. D. (1993). Relative power of the Wilcoxon test, the Friedman test, and repeated-measures ANOVA on ranks. The Journal of Experimental Education, 62, 75–86. https://doi.org/10.1080/00220973.1993.9943832
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Copyright (c) 2022 Pedro Moises de Sousa; Pedro Cunha Carneiro; Gabrielle Macedo Pereira; Mariane Modesto Oliveira; Carlos Aberto da Costa Junior; Luis Vinicius de Moura; Christian Mattjie; Ana Maria da Silva; Túlio Augusto Alves Macedo; Ana Claudia Patrocinio
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