Evaluation of Texture Maps as Input to Extract Deep Features in Glaucoma Diagnosis
Resumo
Glaucoma is a significant cause of blindness in the world. Doctors use computerized images to detect these diseases. Early detection of the disease increases the chances of treatment, reducing the adverse effects. This work proposes an evaluation of texture maps combinations as input to Convolutional Neural Networks for glaucoma classification in retinal images. In our experiments, we used three textures maps, three CNN architectures, and three classifiers. We achieve a Kappa =0.708±0.054 and a Accuracy = 0.859±0.021. We conclude that using the combination of texture maps can improve the automatic detection of glaucoma compared to single-channel inputs, and could be used by state-of-the-art methods to improve their classification rates.
Palavras-chave:
Glaucoma, Convolutional Neural Networks
Referências
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Al-Bander, B., Al-Nuaimy, W., Al-Taee, M. A., and Zheng, Y. (2017). Automated glaucoma diagnosis using deep learning approach. In 2017 14th International Multi-Conference on Systems, Signals & Devices (SSD), pages 207–210. IEEE.
Araujo, J. (2018). Diagnóstico de glaucoma a partir de imagens de fundo de olho utilizando ı́ndices de diversidade. Pós-graduaçao em ciência da computaçao, Universidade Federal do Maranha, Sao Luıs.
Breiman, L. (2001). Random forests. Machine learning, 45(1):5–32.
Breiman, L., Friedman, J., Stone, C. J., and Olshen, R. A. (1984).
Classification and regression trees. CRC press.
Carvalho-Júnior, A. S. V., de Carvalho Filho, A. O., de Sousa, A., and da Silva Barros, P. (2017). Desenvolvimento de métodos para detecção automática do glaucoma. In Anais do XVII Workshop de Informática Médica.SBC.
Dalal, N. and Triggs, B. (2005). Histograms of oriented gradients for human detection.
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. (2009). ImageNet: A Large-Scale Hierarchical Image Database. In CVPR09.
Haralick, R. M. et al. (1979). Statistical and structural approaches to texture. Proceedings of the IEEE, 67(5):786–804.
Huang, G., Liu, Z., and Weinberger, K. Q. (2016). Densely connected convolutional networks. CoRR, abs/1608.06993.
LENSCOPE (2018). Glaucoma: o que é, tipos, sintomas e tratamentos.
Li, Z., He, Y., Keel, S., Meng, W., Chang, R. T., and He, M. (2018). Efficacy of a deep learning system for detecting glaucomatous optic neuropathy based on color fundus photographs. Ophthalmology, 125(8):1199–1206.
Mallat, S. G. (1989). A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis & Machine Intelligence, (7):674–693.
Raghavendra, U., Fujita, H., Bhandary, S. V., Gudigar, A., Tan, J. H., and Acharya, U. R. (2018). Deep convolution neural network for accurate diagnosis of glaucoma using digital fundus images. Information Sciences, 441:41–49.
Rasmussen, C. E. (2003). Gaussian processes in machine learning. In Summer School on Machine Learning, pages 63–71. Springer.
Recommendation, I. 709-6, parameter values for the hdtv standards for production and international programme exchange. 2015.
RIMONE, M. I. A. G. (2015). Rim-one r2.
SILVA, C. C. et al. (2018). Diagnóstico de glaucoma em imagens de fundo de olho utilizando estatı́stica espacial.
SILVA, R. R. V., LOPES, J. G. F., ARAÚJO, F. H. D., Medeiros, F. N. S., and USHIZIMA, D. (2017). Visão Computacional em Python Utilizando as Bibliotecas. Scikit-image e Scikit-learn., volume 1 of III Escola Regional de Informática Computação.
Simonyan, K. and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint ar-convolutional networks for large-scale image recognition. arXiv preprint ar-Xiv:1409.1556.
Stanzione, A., Cuocolo, R., Cocozza, S., Romeo, V., Persico, F., Fusco, F., Longo, N., Brunetti, A., and Imbriaco, M. (2019). Detection of extraprostatic extension of cancer on biparametric mri combining texture analysis and machine learning: preliminary results. Academic radiology.
Thylefors, B. and Negrel, A. (1994). The global impact of glaucoma. Bulletin of the World Health Organization, 72(3):323.
Viera, A. J., Garrett, J. M., et al. (2005). Understanding interobserver agreement: the kappa statistic. Fam med, 37(5):360–363.
Al-Bander, B., Al-Nuaimy, W., Al-Taee, M. A., and Zheng, Y. (2017). Automated glaucoma diagnosis using deep learning approach. In 2017 14th International Multi-Conference on Systems, Signals & Devices (SSD), pages 207–210. IEEE.
Araujo, J. (2018). Diagnóstico de glaucoma a partir de imagens de fundo de olho utilizando ı́ndices de diversidade. Pós-graduaçao em ciência da computaçao, Universidade Federal do Maranha, Sao Luıs.
Breiman, L. (2001). Random forests. Machine learning, 45(1):5–32.
Breiman, L., Friedman, J., Stone, C. J., and Olshen, R. A. (1984).
Classification and regression trees. CRC press.
Carvalho-Júnior, A. S. V., de Carvalho Filho, A. O., de Sousa, A., and da Silva Barros, P. (2017). Desenvolvimento de métodos para detecção automática do glaucoma. In Anais do XVII Workshop de Informática Médica.SBC.
Dalal, N. and Triggs, B. (2005). Histograms of oriented gradients for human detection.
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. (2009). ImageNet: A Large-Scale Hierarchical Image Database. In CVPR09.
Haralick, R. M. et al. (1979). Statistical and structural approaches to texture. Proceedings of the IEEE, 67(5):786–804.
Huang, G., Liu, Z., and Weinberger, K. Q. (2016). Densely connected convolutional networks. CoRR, abs/1608.06993.
LENSCOPE (2018). Glaucoma: o que é, tipos, sintomas e tratamentos.
Li, Z., He, Y., Keel, S., Meng, W., Chang, R. T., and He, M. (2018). Efficacy of a deep learning system for detecting glaucomatous optic neuropathy based on color fundus photographs. Ophthalmology, 125(8):1199–1206.
Mallat, S. G. (1989). A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis & Machine Intelligence, (7):674–693.
Raghavendra, U., Fujita, H., Bhandary, S. V., Gudigar, A., Tan, J. H., and Acharya, U. R. (2018). Deep convolution neural network for accurate diagnosis of glaucoma using digital fundus images. Information Sciences, 441:41–49.
Rasmussen, C. E. (2003). Gaussian processes in machine learning. In Summer School on Machine Learning, pages 63–71. Springer.
Recommendation, I. 709-6, parameter values for the hdtv standards for production and international programme exchange. 2015.
RIMONE, M. I. A. G. (2015). Rim-one r2.
SILVA, C. C. et al. (2018). Diagnóstico de glaucoma em imagens de fundo de olho utilizando estatı́stica espacial.
SILVA, R. R. V., LOPES, J. G. F., ARAÚJO, F. H. D., Medeiros, F. N. S., and USHIZIMA, D. (2017). Visão Computacional em Python Utilizando as Bibliotecas. Scikit-image e Scikit-learn., volume 1 of III Escola Regional de Informática Computação.
Simonyan, K. and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint ar-convolutional networks for large-scale image recognition. arXiv preprint ar-Xiv:1409.1556.
Stanzione, A., Cuocolo, R., Cocozza, S., Romeo, V., Persico, F., Fusco, F., Longo, N., Brunetti, A., and Imbriaco, M. (2019). Detection of extraprostatic extension of cancer on biparametric mri combining texture analysis and machine learning: preliminary results. Academic radiology.
Thylefors, B. and Negrel, A. (1994). The global impact of glaucoma. Bulletin of the World Health Organization, 72(3):323.
Viera, A. J., Garrett, J. M., et al. (2005). Understanding interobserver agreement: the kappa statistic. Fam med, 37(5):360–363.
Publicado
20/10/2020
Como Citar
SILVA, Daniel; SILVA, Romuere.
Evaluation of Texture Maps as Input to Extract Deep Features in Glaucoma Diagnosis. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 17. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 459-470.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2020.12151.
