Uma Avaliação de Arquiteturas de Aprendizado Profundo para a Classificação de Úlceras do Pé Diabético
Resumo
Uma complicação causada pelo diabetes mellitus é o aparecimento de feridas situadas na região dos pés denominadasúlceras do pé diabético. O tratamento tardio pode acarretar o surgimento de infecção ou isquemia da úlcera que, em estado avançado, pode ocasionar a amputação dos membros inferiores. Neste trabalho foi realizado um comparativo do desempenho de diversas arquiteturas de aprendizado profundo pré-treinadas na classificação de imagens deúlceras do pé diabético. Nossa avaliação levou em consideração quatro cenários, três binários Saudável vs Úlceras; Saudável vs Isquemia; Saudável vs Infecção e um multiclasse Saudável vs Úlcera vs Isquemia vs Infecção vs Infecção e Isquemia. Os resultados alcançados demostram que a nossa proposta consegue classificar tais imagens, visto que, nos testes realizados o índice Kappa atingiu valores considerados "Excelentes". Contudo, para o problema multiclasse ainda é necessário aprimorar o uso dessas técnicas.
Referências
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Simonyan, K. and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
Solís-Sánchez, L. O., Ortiz-Rodriguez, J., Castañeda-Miranda, R., Martinez-Blanco, M., Ornelas-Vargas, G., Galván-Tejada, J. I., Galván-Tejada, C. E., Celaya-Padilla, J. M., and Castañeda-Miranda, C. L. (2016). Identification and evaluation on diabetic foot injury by computer vision. IEEE International Conference on Industrial Technology (ICIT), pages 758–762.
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.
Veredas, F., Mesa, H., and Morente, L. (2010). Binary tissue classification on wound images with neural networks and bayesian classifiers. IEEE Transactions on Medical Imaging, 2(29):410–427.
Vogado, L., Veras, R., Aires, K., Araújo, F., Silva, R., Ponti, M., and Tavares, J. M. R. (2021). Diagnosis of leukaemia in blood slides based on a fine-tuned and highly generalisable deep learning model. Sensors, 21(9):2989–2989.
Wang, L., Pedersen, P. C., Agu, E., Strong, D. M., and Tulu, B. (2016). Area determination of diabetic foot ulcer images using a cascaded two-stage svm-based classification. IEEE Transactions on Biomedical Engineering, 64(9):2098–2109.
Yosinski, J., Clune, J., Bengio, Y., and Lipson, H. (2014). How transferable are features in deep neural networks? In Proceedings of the 27th International Conference on Neural Information Processing Systems, volume 2, pages 3320–3328, Cambridge, MA, USA.
Alzubaidi, L., Fadhel, M., Oleiwi, S., Al-Shamma, O., and Zhang, J. (2020). Dfu_qutnet: diabetic foot ulcer classification using novel deep convolutional neural network. Multimedia Tools and Applications, 79(21-22):15655–15677.
Cavanagh, P. R., Lipsky, B. A., Bradbury, A. W., and Botek, G. (2005). Treatment for diabetic foot ulcers. The Lancet, 366(9498):1725–1735.
Goyal, M., Reeves, N. D., Rajbhandari, S., Ahmad, N., Wang, C., and Yap, M. H. (2020). Recognition of ischaemia and infection in diabetic foot ulcers: Dataset and techniques. Computers in Biology and Medicine, 117:103616–103616.
He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 770–778.
He, X., Zemel, R. S., and Carreira-Perpinán, M. A. (2004). Multiscale conditional random fields for image labeling. In Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004., volume 2, pages II– II. IEEE.
Huang, G., Liu, Z., Van Der Maaten, L., andWeinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4700–4708.
Izadyyazdanabadi, M., Belykh, E., Mooney, M., Martirosyan, N., Eschbacher, J., Nakaji, P., Preul, M. C., and Yang, Y. (2018). Convolutional neural networks: ensemble modeling, fine-tuning and unsupervised semantic localization for neurosurgical cle images. Journal of Visual Communication and Image Representation, 54:10–20.
Kornblith, S., Shlens, J., and Le, Q. V. (2019). Do better imagenet models transfer better? In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2661–2671.
Landis, J. R. and Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1):159–174.
Perez, L. and Wang, J. (2017). The effectiveness of data augmentation in image classification using deep learning. arXiv preprint arXiv:1712.04621.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015). Imagenet large scale visual recognition challenge. International Journal of Computer Vision, 115(3):211–252.
Simonyan, K. and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
Solís-Sánchez, L. O., Ortiz-Rodriguez, J., Castañeda-Miranda, R., Martinez-Blanco, M., Ornelas-Vargas, G., Galván-Tejada, J. I., Galván-Tejada, C. E., Celaya-Padilla, J. M., and Castañeda-Miranda, C. L. (2016). Identification and evaluation on diabetic foot injury by computer vision. IEEE International Conference on Industrial Technology (ICIT), pages 758–762.
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.
Veredas, F., Mesa, H., and Morente, L. (2010). Binary tissue classification on wound images with neural networks and bayesian classifiers. IEEE Transactions on Medical Imaging, 2(29):410–427.
Vogado, L., Veras, R., Aires, K., Araújo, F., Silva, R., Ponti, M., and Tavares, J. M. R. (2021). Diagnosis of leukaemia in blood slides based on a fine-tuned and highly generalisable deep learning model. Sensors, 21(9):2989–2989.
Wang, L., Pedersen, P. C., Agu, E., Strong, D. M., and Tulu, B. (2016). Area determination of diabetic foot ulcer images using a cascaded two-stage svm-based classification. IEEE Transactions on Biomedical Engineering, 64(9):2098–2109.
Yosinski, J., Clune, J., Bengio, Y., and Lipson, H. (2014). How transferable are features in deep neural networks? In Proceedings of the 27th International Conference on Neural Information Processing Systems, volume 2, pages 3320–3328, Cambridge, MA, USA.
Publicado
15/06/2021
Como Citar
SANTOS, Francico; VERAS, Rodrigo; SANTOS, Elineide; CLARO, Maila Lima; VOGADO, Luís Henrique; ITO, Márcia; BIANCHI, Andrea.
Uma Avaliação de Arquiteturas de Aprendizado Profundo para a Classificação de Úlceras do Pé Diabético. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 21. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 323-334.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2021.16076.
