Cross-dataset application for fair evaluation of COVID-19 X-ray models
Resumo
Diversos trabalhos foram realizados para o reconhecimento de COVID-19 por meio de imagens de raio-X. Os trabalhos obtinham bom desempenho no reconhecimento das imagens, no entanto, os modelos estão alinhados aos conjuntos de dados utilizados, o que não implica o mesmo desempenho fora do contexto de treino. Deste modo, este trabalho aplicou uma forma justa para avaliar modelos em diferentes cenários. Os resultados demonstraram que os modelos conseguiram distinguir entre diferentes conjuntos de dados de origem diferente, assim, foi determinado que os trabalhos realizados estiveram adaptados ao contexto do conjunto de dados obtido.
Palavras-chave:
raio-x, avaliação, modelo, coronavírus, protocolo
Referências
Altan, A. and Karasu, S. (2020). Recognition of covid-19 disease from x-ray images by hybrid model consisting of 2d curvelet transform, chaotic salp swarm algorithm and deep learning technique. Chaos, Solitons & Fractals, 140:110071.
Cohen, J. P., Morrison, P., and Dao, L. (2020). Covid-19 image data collection. arXiv preprint arXiv:2003.1159.
Diment, A. and Virtanen, T. (2017). Transfer learning of weakly labelled audio. In 2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), pages 6–10. IEEE.
Guarrasi, V., D’Amico, N. C., Sicilia, R., Cordelli, E., and Soda, P. (2022). Pareto optimization of deep networks for covid-19 diagnosis from chest x-rays. Pattern Recognition, 121:108242.
He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 770–778.
Irvin, J., Rajpurkar, P., Ko, M., Yu, Y., Ciurea-Ilcus, S., Chute, C., Marklund, H., Haghgoo, B., Ball, R., Shpanskaya, K., Seekins, J., Mong, D. A., Halabi, S. S., Sandberg, J. K., Jones, R., Larson, D. B., Langlotz, C. P., Patel, B. N., Lungren, M. P., and Ng, A. Y. (2019). Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01):590–597.
Maguolo, G. and Nanni, L. (2021). A critic evaluation of methods for covid-19 automatic detection from x-ray images. Information Fusion, 76:1–7.
Mooney, P. (2017). Chest x-ray images (pneumonia). [link]. Acesso em: 10 ago. 2023.
Shorten, C., Khoshgoftaar, T. M., and Furht, B. (2021). Deep learning applications for covid-19. Journal of big Data, 8(1):1–54.
Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., and Summers, R. M. (2017). Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
Cohen, J. P., Morrison, P., and Dao, L. (2020). Covid-19 image data collection. arXiv preprint arXiv:2003.1159.
Diment, A. and Virtanen, T. (2017). Transfer learning of weakly labelled audio. In 2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), pages 6–10. IEEE.
Guarrasi, V., D’Amico, N. C., Sicilia, R., Cordelli, E., and Soda, P. (2022). Pareto optimization of deep networks for covid-19 diagnosis from chest x-rays. Pattern Recognition, 121:108242.
He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 770–778.
Irvin, J., Rajpurkar, P., Ko, M., Yu, Y., Ciurea-Ilcus, S., Chute, C., Marklund, H., Haghgoo, B., Ball, R., Shpanskaya, K., Seekins, J., Mong, D. A., Halabi, S. S., Sandberg, J. K., Jones, R., Larson, D. B., Langlotz, C. P., Patel, B. N., Lungren, M. P., and Ng, A. Y. (2019). Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01):590–597.
Maguolo, G. and Nanni, L. (2021). A critic evaluation of methods for covid-19 automatic detection from x-ray images. Information Fusion, 76:1–7.
Mooney, P. (2017). Chest x-ray images (pneumonia). [link]. Acesso em: 10 ago. 2023.
Shorten, C., Khoshgoftaar, T. M., and Furht, B. (2021). Deep learning applications for covid-19. Journal of big Data, 8(1):1–54.
Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., and Summers, R. M. (2017). Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
Publicado
28/11/2023
Como Citar
PEDROSO, Gabriel de S. G.; VENTURA, Thiago M.; DE OLIVEIRA, Allan G..
Cross-dataset application for fair evaluation of COVID-19 X-ray models. In: ESCOLA REGIONAL DE INFORMÁTICA DE MATO GROSSO (ERI-MT), 12. , 2023, Cuiabá/MT.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 226-230.
ISSN 2447-5386.
DOI: https://doi.org/10.5753/eri-mt.2023.236474.
