Characterization Extraction Learning Applied to Glaucoma Diagnosis
Abstract
Glaucoma is an asymptomatic disease that can bring people to blind- ness if not early detected. Computational intelligence methods have been pro- posed to provide a computerized diagnosis that can guide patients to the ap- propriate treatment. However, these techniques face methodology optmization problems, which depends on the choices of many algorithms from diferent kno- wledge areas. This paper suggests a solution through meta-learning of pre- processing methods, decomposition and features extraction which have to be used efficiently in order to solve the problem. Current results are promissing, reaching 91.24% accuracy after 50 evaluations and it is suposed to improve proportionally to the number of evaluations.
References
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de Sousa, J. A., de Paiva, A. C., Sousa de Almeida, J. D., Silva, A. C., Junior, G. B., and Gattass, M. (2017). Texture based on geostatistic for glaucoma diagnosis from fundus eye image. Multimedia Tools and Applications, 76(18):19173–19190. http://dx.doi.org/10.1007/s11042-017-4608-y
Fumero, F., Alayon, S., Sanchez, J. L., Sigut, J., and Gonzalez-Hernandez, M. (2011). RIM-ONE: An open retinal image database for optic nerve evaluation. In 2011 24th International Symposium on Computer-Based Medical Systems (CBMS), pages 1–6, Bristol, UK. IEEE. http://dx.doi.org/10.1109/CBMS.2011.5999143
Gonzalez, R. C. and Woods, R. E. (2010). Processamento Digital de Imagens. Pearson, 3 a edition. Haralick, R., Shanmugam, K., and Dinstein, I. (1973). Texture features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, 3(6). http://dx.doi.org/10.1109/TSMC.1973.4309314
Hinton, G. E., Osindero, S., and Teh, Y.-W. (2006). A fast learning algorithm for deep belief nets. Neural Comput., 18(7):1527–1554. http://dx.doi.org/10.1162/neco.2006.18.7.1527
Kawde, M. M. and Bairagi, V. K. (2016). Early detection of glaucoma disease using image processing. Indian Journal of Science and Technology, 9(30).
Kim, S. J., Cho, K. J., and Oh, S. (2017). Development of machine learning models for diagnosis of glaucoma. PLOS ONE, 12(5):1–16. http://dx.doi.org/10.1371/journal.pone.0177726
Lecun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278–2324.
Matheus V. L. Ribeiro, E. O. T. S. (2017). Lbp incoerente como nova proposta para descrição de cenas.
Ranzato, M. and Hinton, G. E. (2010). Modeling pixel means and covariances using factorized third-order boltzmann machines. In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 2551–2558.
Silva, M. G., Pessoa, A. C., de Almeida, J. D., Junior, G. B., and de Paiva, A. C. (2018). Diagnóstico do glaucoma em imagens de retinografia usando variantes de padrões locais binários. In 18 o Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS 2018), volume 18. SBC.
Silveira, R. M., Almeida, J. D., Teixeira, J. A., Maia, I. M., Paiva, A. C., and Júnior, G. B. (2018). Dispositivo de baixo custo para detecção de patologias da visão. In 18 o Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS 2018), volume 18. SBC.
T. Ojala, M. Pietikainen, D. H. (1996). A comparative study of texture measures with classification based on featured distribution. Pattern Recognit, 29:51–59. http://dx.doi.org/10.1016/0031-3203(95)00067-4
Published
2019-06-11
How to Cite
FERNANDES, Arthur Guilherme Santos; MARTINS, Caio Manfredini da Silva; LIMA, Alan Carlos de Moura; JUNIOR, Geraldo Braz; DE ALMEIDA, João Dallyson Sousa; DE PAIVA, Anselmo Cardoso.
Characterization Extraction Learning Applied to Glaucoma Diagnosis. In: BRAZILIAN SYMPOSIUM ON COMPUTING APPLIED TO HEALTH (SBCAS), 19. , 2019, Niterói.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 342-347.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2019.6273.
