Um Método Baseado em Radiomics e MLP para Diagnóstico Automático de COVID-19 a partir de Raio-X de Tórax
Resumo
A identificação da COVID-19 é um fator crucial para o tratamento e cura dos pacientes. Com o avanço da tecnologia, tornou-se possível o desenvolvimento de métodos computacionais capazes de auxiliar os especialistas na tarefa de análise de imagens médicas. Portanto, o presente trabalho tem como objetivo desenvolver um método automático de diagnóstico da COVID-19 por meio de imagens de raio-X do tórax usando uma abordagem Radiomics e o algoritmo Multi-Layer Perceptron. O método proposto foi avaliado em duas bases de imagens públicas, obtendo uma acurácia de 96,8%, precisão de 97,1%, recall de 96,4% e especificidade de 97,9%, com isso, demonstrando a eficácia no diagnóstico automático da COVID-19 por imagem.
Referências
Apostolopoulos, I. D. and Mpesiana, T. A. (2020). Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. Physical and Engineering Sciences in Medicine, page 1.
Bakheet, S. and Al-Hamadi, A. (2021). Automatic detection of covid-19 using pruned glcm-based texture features and ldcrf classification. Computers in Biology and Medicine, 137:104781.
Bernheim, A., Mei, X., Huang, M., Yang, Y., Fayad, Z. A., Zhang, N., Diao, K., Lin, B., Zhu, X., Li, K., et al. (2020). Chest ct findings in coronavirus disease-19 (covid-19): relationship to duration of infection. Radiology, page 200463.
Chan, J. F.-W., Yuan, S., Kok, K.-H., To, K. K.-W., Chu, H., Yang, J., Xing, F., Liu, J., Yip, C. C.-Y., Poon, R. W.-S., et al. (2020). A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster. The Lancet, 395(10223):514-523.
dos S. Neto, A. C., Diniz, J. O. B., Diniz, P. H. B., Cavalcante, A. B., Silva, A. C., and de Paiva, A. C. (2018). Classificação do câncer de pulmão de células não pequenas usando índice de diversidade filogenética e índices de forma em uma abordagem radiomics. In Anais do XVIII Simpósio Brasileiro de Computação Aplicada à Saúde, Porto Alegre, RS, Brasil. SBC.
El-Din Hemdan, E., Shouman, M. A., and Karar, M. E. (2020). Covidx-net: A framework of deep learning classifiers to diagnose covid-19 in x-ray images. arXiv, pages arXiv-2003.
Hamilton, N. A., Pantelic, R. S., Hanson, K., and Teasdale, R. D. (2007). Fast automated cell phenotype image classification. BMC bioinformatics, 8(1):1-8.
Haralick, R. M., Shanmugam, K., and Dinstein, I. H. (1973). Textural features for image classification. IEEE Transactions on systems, man, and cybernetics, (6):610-621.
Imada, R. (2013). Os Momentos de Zernike no Reconhecimento de Contornos de Edifícios. Anais do Congresso de Matemática Aplicada e Computacional.
Kroft, L. J., van der Velden, L., Girón, I. H., Roelofs, J. J., de Roos, A., and Geleijns, J. (2019). Added value of ultra-low-dose computed tomography, dose equivalent to chest x-ray radiography, for diagnosing chest pathology. Journal of thoracic imaging, 34(3):179.
Kumar, A., Tripathi, A. R., Satapathy, S. C., and Zhang, Y.-D. (2022). Sars-net: Covid-19 detection from chest x-rays by combining graph convolutional network and convolutional neural network. Pattern Recognition, 122:108255.
Mayerhoefer, M. E., Materka, A., Langs, G., Häggström, I., Szczypiński, P., Gibbs, P., and Cook, G. (2020). Introduction to radiomics. Journal of Nuclear Medicine, 61(4):488-495.
Narin, A., Kaya, C., and Pamuk, Z. (2020). Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks. arXiv preprint arXiv:2003.10849.
Ozturk, T., Talo, M., Yildirim, E. A., Baloglu, U. B., Yildirim, O., and Acharya], U. R. (2020). Automated detection of covid-19 cases using deep neural networks with x-ray images. Computers in Biology and Medicine, 121:103792.
Pereira, R. M., Bertolini, D., Teixeira, L. O., Silla, C. N., and Costa, Y. M. (2020). Covid-19 identification in chest x-ray images on flat and hierarchical classification scenarios. Computer Methods and Programs in Biomedicine, 194:105532.
Sitaula, C. and Hossain, M. B. (2021). Attention-based vgg-16 model for covid-19 chest x-ray image classification. Applied Intelligence, 51(5):2850-2863.
Sonawane, J. S. and Patil, D. (2014). Prediction of heart disease using multilayer perceptron neural network. In International conference on information communication and embedded systems (ICICES2014), pages 1-6. IEEE.
Tan, W., Zhao, X., Ma, X., Wang, W., Niu, P., Xu, W., Gao, G. F., and Wu, G. (2020). A novel coronavirus genome identified in a cluster of pneumonia cases-wuhan, china 20192020. China CDC Weekly, 2(4):61-62.
Wu, Z., Li, L., Jin, R., Liang, L., Hu, Z., Tao, L., Han, Y., Feng, W., Zhou, D., Li, W., et al. (2021). Texture feature-based machine learning classifier could assist in the diagnosis of covid-19. European journal of radiology, 137:109602.
Zhang, J., Xie, Y., Li, Y., Shen, C., and 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., Li, X., Yang, B., Song, J., Zhao, X., Huang, B., Shi, W., Lu, R., et al. (2020). China novel coronavirus investigating and research team. a novel coronavirus from patients with pneumonia in china, 2019. N Engl J Med, 382(8):727-733.
Zwanenburg, A., Valliéres, M., Abdalah, M. A., Aerts, H. J. W. L., Andrearczyk, V., Apte, A., Ashrafinia, S., Bakas, S., Beukinga, R. J., Boellaard, R., Bogowicz, M., Boldrini, L., Buvat, I., Cook, G. J. R., Davatzikos, C., Depeursinge, A., Desseroit, M.-C., Dinapoli, N., Dinh, C. V., Echegaray, S., Naqa, I. E., Fedorov, A. Y., Gatta, R., Gillies, R. J., Goh, V., Götz, M., Guckenberger, M., Ha, S. M., Hatt, M., Isensee, F., Lambin, P., Leger, S., Leijenaar, R. T., Lenkowicz, J., Lippert, F., Losnegård, A., Maier-Hein, K. H., Morin, O., Müller, H., Napel, S., Nioche, C., Orlhac, F., Pati, S., Pfaehler, E. A., Rahmim, A., Rao, A. U., Scherer, J., Siddique, M. M., Sijtsema, N. M., Fernandez, J. S., Spezi, E., Steenbakkers, R. J., Tanadini-Lang, S., Thorwarth, D., Troost, E. G., Upadhaya, T., Valentini, V., van Dijk, L. V., van Griethuysen, J., van Velden, F. H., Whybra, P., Richter, C., and Löck, S. (2020). The image biomarker standardization initiative: Standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology, 295(2):328-338.
Bakheet, S. and Al-Hamadi, A. (2021). Automatic detection of covid-19 using pruned glcm-based texture features and ldcrf classification. Computers in Biology and Medicine, 137:104781.
Bernheim, A., Mei, X., Huang, M., Yang, Y., Fayad, Z. A., Zhang, N., Diao, K., Lin, B., Zhu, X., Li, K., et al. (2020). Chest ct findings in coronavirus disease-19 (covid-19): relationship to duration of infection. Radiology, page 200463.
Chan, J. F.-W., Yuan, S., Kok, K.-H., To, K. K.-W., Chu, H., Yang, J., Xing, F., Liu, J., Yip, C. C.-Y., Poon, R. W.-S., et al. (2020). A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster. The Lancet, 395(10223):514-523.
dos S. Neto, A. C., Diniz, J. O. B., Diniz, P. H. B., Cavalcante, A. B., Silva, A. C., and de Paiva, A. C. (2018). Classificação do câncer de pulmão de células não pequenas usando índice de diversidade filogenética e índices de forma em uma abordagem radiomics. In Anais do XVIII Simpósio Brasileiro de Computação Aplicada à Saúde, Porto Alegre, RS, Brasil. SBC.
El-Din Hemdan, E., Shouman, M. A., and Karar, M. E. (2020). Covidx-net: A framework of deep learning classifiers to diagnose covid-19 in x-ray images. arXiv, pages arXiv-2003.
Hamilton, N. A., Pantelic, R. S., Hanson, K., and Teasdale, R. D. (2007). Fast automated cell phenotype image classification. BMC bioinformatics, 8(1):1-8.
Haralick, R. M., Shanmugam, K., and Dinstein, I. H. (1973). Textural features for image classification. IEEE Transactions on systems, man, and cybernetics, (6):610-621.
Imada, R. (2013). Os Momentos de Zernike no Reconhecimento de Contornos de Edifícios. Anais do Congresso de Matemática Aplicada e Computacional.
Kroft, L. J., van der Velden, L., Girón, I. H., Roelofs, J. J., de Roos, A., and Geleijns, J. (2019). Added value of ultra-low-dose computed tomography, dose equivalent to chest x-ray radiography, for diagnosing chest pathology. Journal of thoracic imaging, 34(3):179.
Kumar, A., Tripathi, A. R., Satapathy, S. C., and Zhang, Y.-D. (2022). Sars-net: Covid-19 detection from chest x-rays by combining graph convolutional network and convolutional neural network. Pattern Recognition, 122:108255.
Mayerhoefer, M. E., Materka, A., Langs, G., Häggström, I., Szczypiński, P., Gibbs, P., and Cook, G. (2020). Introduction to radiomics. Journal of Nuclear Medicine, 61(4):488-495.
Narin, A., Kaya, C., and Pamuk, Z. (2020). Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks. arXiv preprint arXiv:2003.10849.
Ozturk, T., Talo, M., Yildirim, E. A., Baloglu, U. B., Yildirim, O., and Acharya], U. R. (2020). Automated detection of covid-19 cases using deep neural networks with x-ray images. Computers in Biology and Medicine, 121:103792.
Pereira, R. M., Bertolini, D., Teixeira, L. O., Silla, C. N., and Costa, Y. M. (2020). Covid-19 identification in chest x-ray images on flat and hierarchical classification scenarios. Computer Methods and Programs in Biomedicine, 194:105532.
Sitaula, C. and Hossain, M. B. (2021). Attention-based vgg-16 model for covid-19 chest x-ray image classification. Applied Intelligence, 51(5):2850-2863.
Sonawane, J. S. and Patil, D. (2014). Prediction of heart disease using multilayer perceptron neural network. In International conference on information communication and embedded systems (ICICES2014), pages 1-6. IEEE.
Tan, W., Zhao, X., Ma, X., Wang, W., Niu, P., Xu, W., Gao, G. F., and Wu, G. (2020). A novel coronavirus genome identified in a cluster of pneumonia cases-wuhan, china 20192020. China CDC Weekly, 2(4):61-62.
Wu, Z., Li, L., Jin, R., Liang, L., Hu, Z., Tao, L., Han, Y., Feng, W., Zhou, D., Li, W., et al. (2021). Texture feature-based machine learning classifier could assist in the diagnosis of covid-19. European journal of radiology, 137:109602.
Zhang, J., Xie, Y., Li, Y., Shen, C., and 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., Li, X., Yang, B., Song, J., Zhao, X., Huang, B., Shi, W., Lu, R., et al. (2020). China novel coronavirus investigating and research team. a novel coronavirus from patients with pneumonia in china, 2019. N Engl J Med, 382(8):727-733.
Zwanenburg, A., Valliéres, M., Abdalah, M. A., Aerts, H. J. W. L., Andrearczyk, V., Apte, A., Ashrafinia, S., Bakas, S., Beukinga, R. J., Boellaard, R., Bogowicz, M., Boldrini, L., Buvat, I., Cook, G. J. R., Davatzikos, C., Depeursinge, A., Desseroit, M.-C., Dinapoli, N., Dinh, C. V., Echegaray, S., Naqa, I. E., Fedorov, A. Y., Gatta, R., Gillies, R. J., Goh, V., Götz, M., Guckenberger, M., Ha, S. M., Hatt, M., Isensee, F., Lambin, P., Leger, S., Leijenaar, R. T., Lenkowicz, J., Lippert, F., Losnegård, A., Maier-Hein, K. H., Morin, O., Müller, H., Napel, S., Nioche, C., Orlhac, F., Pati, S., Pfaehler, E. A., Rahmim, A., Rao, A. U., Scherer, J., Siddique, M. M., Sijtsema, N. M., Fernandez, J. S., Spezi, E., Steenbakkers, R. J., Tanadini-Lang, S., Thorwarth, D., Troost, E. G., Upadhaya, T., Valentini, V., van Dijk, L. V., van Griethuysen, J., van Velden, F. H., Whybra, P., Richter, C., and Löck, S. (2020). The image biomarker standardization initiative: Standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology, 295(2):328-338.
Publicado
28/09/2022
Como Citar
SOUSA FILHO, Emanuel L. C. de; MAGNO, Paulo G. S.; SANTOS, Camyla J. P.; SILVA, Giovanni L. F. da; DINIZ, João O. B.; QUINTANILHA, Darlan B. P..
Um Método Baseado em Radiomics e MLP para Diagnóstico Automático de COVID-19 a partir de Raio-X de Tórax. In: ESCOLA REGIONAL DE COMPUTAÇÃO DO CEARÁ, MARANHÃO E PIAUÍ (ERCEMAPI), 10. , 2022, São Luís.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 188-197.
DOI: https://doi.org/10.5753/ercemapi.2022.226508.
