PavicNet-MC: Um modelo de classificação multilabel aplicado em ultrassonografia pulmonar
Resumo
Nos últimos anos as consequências da Covid-19 e outras doenças pulmonares vem causando um aumento na demanda pelos serviços de saúde, o diagnóstico precoce e preciso dessas doenças é essencial para a recuperação dos pacientes. Este artigo propõe um modelo de classificação multirrótulo, denominado PavicNet-MC. Este modelo foi desenvolvido com a motivação de identificar cinco características visíveis em ultrassonografia pulmonar. O modelo proposto obteve uma precisão de 99% na classificação das cinco características. Resultados mostram que o modelo proposto é altamente eficaz na detecção e monitoramento das características visíveis que se correlacionam com doenças pulmonares, e possui uma complexidade relativamente baixa em comparação com outras arquiteturas encontradas na literatura.Referências
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Moore, S. and Gardiner, E. (2020). Point of care and intensive care lung ultrasound: a reference guide for practitioners during covid-19. Radiography, 26(4):e297–e302.
Saraogi, A. (2015). Lung ultrasound: Present and future. Lung India: Official Organ of Indian Chest Society, 32(3):250.
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2818–2826.
Ullah, N., Khan, J. A., Almakdi, S., Khan, M. S., Alshehri, M., Alboaneen, D., and Raza, A. (2022). A novel coviddetnet deep learning model for effective covid-19 infection detection using chest radiograph images. Applied Sciences, 12(12):6269.
Wang, Y., Ge, X., Ma, H., Qi, S., Zhang, G., and Yao, Y. (2021). Deep learning in medical ultrasound image analysis: a review. IEEE Access, 9:54310–54324.
Zayet, S., Lepiller, Q., Zahra, H., Royer, P.-Y., Toko, L., Gendrin, V., Klopfenstein, T., et al. (2020). Clinical features of covid-19 and influenza: a comparative study on nord franche-comte cluster. Microbes and infection, 22(9):481–488.
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Bhoil, R., Ahluwalia, A., Chopra, R., Surya, M., and Bhoil, S. (2021). Signs and lines in lung ultrasound. Journal of Ultrasonography, 21(86):225–233.
Born, J., Brändle, G., Cossio, M., Disdier, M., Goulet, J., Roulin, J., and Wiedemann, N. (2020). Pocovid-net: automatic detection of covid-19 from a new lung ultrasound imaging dataset (pocus). arXiv preprint arXiv:2004.12084.
Brunese, L., Mercaldo, F., Reginelli, A., and Santone, A. (2020). Explainable deep learning for pulmonary disease and coronavirus covid-19 detection from x-rays. Computer Methods and Programs in Biomedicine, 196:105608.
Delardas, O., Kechagias, K. S., Pontikos, P. N., and Giannos, P. (2022). Socio-economic impacts and challenges of the coronavirus pandemic (covid-19): An updated review. Sustainability, 14(15):9699.
Diaz-Escobar, J., Ordóñez-Guillén, N. E., Villarreal-Reyes, S., Galaviz-Mosqueda, A., Kober, V., Rivera-Rodriguez, R., and Rizk, J. E. L. (2021). Deep-learning based detection of covid-19 using lung ultrasound imagery. Plos one, 16(8):e0255886.
Fourcade, A. and Khonsari, R. H. (2019). Deep learning in medical image analysis: A third eye for doctors. Journal of stomatology, oral and maxillofacial surgery, 120(4):279–288.
Gehmacher, O., Mathis, G., Kopf, A., and Scheier, M. (1995). Ultrasound imaging of pneumonia. Ultrasound in medicine & biology, 21(9):1119–1122.
Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4700–4708.
Lichtenstein, D. A. and Meziere, G. A. (2008). Relevance of lung ultrasound in the diagnosis of acute respiratory failure*: the blue protocol. Chest, 134(1):117–125.
Lichtenstein, D. A., Mezière, G. A., Lagoueyte, J.-F., Biderman, P., Goldstein, I., and Gepner, A. (2009). A-lines and b-lines: lung ultrasound as a bedside tool for predicting pulmonary artery occlusion pressure in the critically ill. Chest, 136(4):1014–1020.
Liu, S., Wang, Y., Yang, X., Lei, B., Liu, L., Li, S. X., Ni, D., and Wang, T. (2019). Deep learning in medical ultrasound analysis: a review. Engineering, 5(2):261–275.
Moore, S. and Gardiner, E. (2020). Point of care and intensive care lung ultrasound: a reference guide for practitioners during covid-19. Radiography, 26(4):e297–e302.
Saraogi, A. (2015). Lung ultrasound: Present and future. Lung India: Official Organ of Indian Chest Society, 32(3):250.
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2818–2826.
Ullah, N., Khan, J. A., Almakdi, S., Khan, M. S., Alshehri, M., Alboaneen, D., and Raza, A. (2022). A novel coviddetnet deep learning model for effective covid-19 infection detection using chest radiograph images. Applied Sciences, 12(12):6269.
Wang, Y., Ge, X., Ma, H., Qi, S., Zhang, G., and Yao, Y. (2021). Deep learning in medical ultrasound image analysis: a review. IEEE Access, 9:54310–54324.
Zayet, S., Lepiller, Q., Zahra, H., Royer, P.-Y., Toko, L., Gendrin, V., Klopfenstein, T., et al. (2020). Clinical features of covid-19 and influenza: a comparative study on nord franche-comte cluster. Microbes and infection, 22(9):481–488.
Zhao, L. and Lediju Bell, M. A. (2022). A review of deep learning applications in lung ultrasound imaging of covid-19 patients. BME Frontiers, 2022.
Publicado
27/06/2023
Como Citar
SILVA E SILVA, Clécio Elias; MACHADO, Salomão Mafalda; ALVAREZ, Ana Beatriz; CHAVEZ, Roger Fredy Larico.
PavicNet-MC: Um modelo de classificação multilabel aplicado em ultrassonografia pulmonar. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 23. , 2023, São Paulo/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 395-406.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2023.230067.
