Impacto da Resolução na Detecção de Retinopatia Diabética com uso de Deep Learning
Resumo
No contexto dos sistemas de saúde, a prevenção é uma das maneiras mais eficazes evitar a progressão de doenças. Muitas delas podem ser tratadas quando diagnosticadas em estágio inicial. A demanda por exames preventivos está aumentando e não consegue-se atender essa demanda com eficiência pelos médicos sobrecarregados. Portanto, existe a necessidade de uma metodologia para automatizar e aumentar a eficiência de exames de triagem. Neste artigo, discutimos o desenvolvimento de redes neurais convolucionais profundas para detectar retinopatia diabética, e o impacto da resolução de imagem e da rede na precisão de predição. Atingimos 0,93 de área sob a curva de característica operacional do receptor ao aumentar a resolução da arquitetura inception v3.
Referências
Cuadros, J. and Bresnick, G. (2009). Eyepacs: an adaptable telemedicine system for diabetic retinopathy screening. Journal of diabetes science and technology, 3(3):509–516.
Decenciere, E., Zhang, X., Cazuguel, G., Lay, B., Cochener, B., Trone, C., Gain, P.,` Ordonez, R., Massin, P., Erginay, A., et al. (2014). Feedback on a publicly distributed image database: the messidor database. Image Analysis & Stereology, 33(3):231–234.
Gargeya, R. and Leng, T. (2017). Automated identification of diabetic retinopathy using deep learning. Ophthalmology, 124(7):962–969.
Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. Jama, 316(22):2402–2410.
Hornik, K., Stinchcombe, M., and White, H. (1989). Multilayer feedforward networks are universal approximators. Neural networks, 2(5):359–366.
Krause, J., Gulshan, V., Rahimy, E., Karth, P., Widner, K., Corrado, G. S., Peng, L., and Webster, D. R. (2018). Grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy. Ophthalmology, 125(8):1264–1272.
LeCun, Y., Bengio, Y., et al. (1995). Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks, 3361(10):1995.
Pratt, H., Coenen, F., Broadbent, D. M., Harding, S. P., and Zheng, Y. (2016). Convolutional neural networks for diabetic retinopathy. Procedia Computer Science, 90:200–205.
Resnikoff, S., Felch, W., Gauthier, T.-M., and Spivey, B. (2012). The number of ophthalmologists in practice and training worldwide: a growing gap despite more than 200 000 practitioners. British Journal of Ophthalmology, 96(6):783–787.
Stratton, I. M., Adler, A. I., Neil, H. A. W., Matthews, D. R., Manley, S. E., Cull, C. A., Hadden, D., Turner, R. C., and Holman, R. R. (2000). Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes (ukpds 35): prospective observational study. Bmj, 321(7258):405–412.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1–9.
Telo, G. H., Cureau, F. V., de Souza, M. S., Andrade, T. S., Copes, F., and Schaan, B. D. (2016). Prevalence of diabetes in brazil over time: a systematic review with meta-analysis. Diabetology & metabolic syndrome, 8(1):65.
Tufail, A., Rudisill, C., Egan, C., Kapetanakis, V. V., Salas-Vega, S., Owen, C. G., Lee, A., Louw, V., Anderson, J., Liew, G., et al. (2017). Automated diabetic retinopathy image assessment software: diagnostic accuracy and cost-effectiveness compared with human graders. Ophthalmology, 124(3):343–351.
Voets, M., Møllersen, K., and Bongo, L. A. (2018). Replication study: Development and validation of deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. arXiv preprint arXiv:1803.04337.
Wong, T. Y. and Bressler, N. M. (2016). Artificial intelligence with deep learning technology looks into diabetic retinopathy screening. Jama, 316(22):2366–2367.
Decenciere, E., Zhang, X., Cazuguel, G., Lay, B., Cochener, B., Trone, C., Gain, P.,` Ordonez, R., Massin, P., Erginay, A., et al. (2014). Feedback on a publicly distributed image database: the messidor database. Image Analysis & Stereology, 33(3):231–234.
Gargeya, R. and Leng, T. (2017). Automated identification of diabetic retinopathy using deep learning. Ophthalmology, 124(7):962–969.
Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. Jama, 316(22):2402–2410.
Hornik, K., Stinchcombe, M., and White, H. (1989). Multilayer feedforward networks are universal approximators. Neural networks, 2(5):359–366.
Krause, J., Gulshan, V., Rahimy, E., Karth, P., Widner, K., Corrado, G. S., Peng, L., and Webster, D. R. (2018). Grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy. Ophthalmology, 125(8):1264–1272.
LeCun, Y., Bengio, Y., et al. (1995). Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks, 3361(10):1995.
Pratt, H., Coenen, F., Broadbent, D. M., Harding, S. P., and Zheng, Y. (2016). Convolutional neural networks for diabetic retinopathy. Procedia Computer Science, 90:200–205.
Resnikoff, S., Felch, W., Gauthier, T.-M., and Spivey, B. (2012). The number of ophthalmologists in practice and training worldwide: a growing gap despite more than 200 000 practitioners. British Journal of Ophthalmology, 96(6):783–787.
Stratton, I. M., Adler, A. I., Neil, H. A. W., Matthews, D. R., Manley, S. E., Cull, C. A., Hadden, D., Turner, R. C., and Holman, R. R. (2000). Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes (ukpds 35): prospective observational study. Bmj, 321(7258):405–412.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1–9.
Telo, G. H., Cureau, F. V., de Souza, M. S., Andrade, T. S., Copes, F., and Schaan, B. D. (2016). Prevalence of diabetes in brazil over time: a systematic review with meta-analysis. Diabetology & metabolic syndrome, 8(1):65.
Tufail, A., Rudisill, C., Egan, C., Kapetanakis, V. V., Salas-Vega, S., Owen, C. G., Lee, A., Louw, V., Anderson, J., Liew, G., et al. (2017). Automated diabetic retinopathy image assessment software: diagnostic accuracy and cost-effectiveness compared with human graders. Ophthalmology, 124(3):343–351.
Voets, M., Møllersen, K., and Bongo, L. A. (2018). Replication study: Development and validation of deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. arXiv preprint arXiv:1803.04337.
Wong, T. Y. and Bressler, N. M. (2016). Artificial intelligence with deep learning technology looks into diabetic retinopathy screening. Jama, 316(22):2366–2367.
Publicado
15/09/2020
Como Citar
MOREIRA, Francis; SCHAAN, Beatriz; SCHNEIDERS, Josiane; REIS, Mateus; SERPA, Matheus; NAVAUX, Philippe.
Impacto da Resolução na Detecção de Retinopatia Diabética com uso de Deep Learning. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 20. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 494-499.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2020.11546.
