Classification of brain lesions on magnetic resonance imaging using superpixel, PSO and convolutional neural network
Resumo
One of the most severe and common brain tumors is gliomas. Manual classification of injuries of this type is a laborious task in the clinical routine. Therefore, this work proposes an automatic method to classify lesions in the brain in 3D MR images based on superpixels, PSO algorithm and convolutional neural network. The proposed method obtained results for the complete, central and active regions, an accuracy of 87.88%, 70.51%, 80.08% and precision of 76%, 84%, 75% for the respective regions. The results demonstrate the difficulty of the network in the classification of the regions found in the lesions.
Palavras-chave:
brain tumor classification, magnetic resonance imaging, convolutional neural networks, particle swarm optimization, superpixel
Referências
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G. L. F. d. Silva, T. L. A. Valente, A. C. Silva, A. C. de Paiva, and M. Gattass, Convolutional neural network-based pso for lung nodule false positive reduction on ct images, Computer methods and programs in biomedicine, vol. 162, pp. 109 - 118, 2018. DOI: 10.1016/j.cmpb.2018.05.006
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M. L. Goodenberger and R. B. Jenkins, Genetics of adult glioma, Cancer genetics, vol. 205, no. 12, pp. 613 - 621, 2012. DOI: 10.1016/j.cancergen.2012.10.009
M. Havaei, A. Davy, D. Warde-Farley, A. Biard, A. Courville, Y. Bengio, C. Pal, P.-M. Jodoin, and H. Larochelle, Brain tumor segmentation with deep neural networks, Medical image analysis, vol. 35, pp. 18 - 31, 2017. DOI: 10.1016/j.media.2016.05.004
B. Stewart, C. P. Wild et al., World cancer report 2014.
A. Isin, C. Direkoglu, and M. Sah, Review of mri-based brain tumor image segmentation using deep learning methods, Procedia Computer Science, vol. 102, pp. 317 - 324, 2016. DOI: 10.1016/j.procs.2016.09.407
S. Pereira, A. Pinto, V. Alves, and C. A. Silva, Brain tumor segmentation using convolutional neural networks in mri images, IEEE transactions on medical imaging, vol. 35, no. 5, pp. 1240 - 1251, 2016. DOI: 10.1109/tmi.2016.2538465
L. Lefkovits, S. Lefkovits, and M.-F. Vaida, Brain tumor segmentation based on random forest, Memoirs of the Scientific Sections of the Romanian Academy, vol. 39, no. 1, pp. 83 - 93, 2016.
B. H. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. Farahani, J. Kirby, Y. Burren, N. Porz, J. Slotboom, R. Wiest et al., The multimodal brain tumor image segmentation benchmark (brats), IEEE transactions on medical imaging, vol. 34, no. 10, p. 1993, 2015.
-- UCSF, Magnetic resonance imaging (mri), https://radiology.ucsf.edu/patient-care/ services/mriaccordion-whatare-the-risks-of- contrast, 2019, accessed: 2019 -07-01.
R. C. Gonzalez and R. C. Woods, Processamento digital de imagens. Pearson Prentice Hall, 2010.
R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, S. Süsstrunk et al., Slic superpixels compared to state-of-the-art superpixel methods, IEEE transactions on pattern analysis and machine intelligence, vol. 34, no. 11, pp. 2274 - 2282, 2012.
P. H. B. Diniz, T. L. Azevedo Valente, J. O. Bandeira Diniz, A. C. Silva, M. Gattass, N. Ventura, B. C. Muniz, and E. L. Gasparetto, Detection of white matter lesion regions in mri using slico and convolutional neural network, Computer methods and programs in biomedicine, vol. 167, pp. 49 - 63, 2018. DOI: 10.1016/j.cmpb.2018.04.011
R. Eberhart and J. Kennedy, A new optimizer using particle swarm theory, in Micro Machine and Human Science, MHS' 95., Proceedings of the Sixth International Symposium on. IEEE, 1995, pp. 39 - 43. DOI: 10.1109/mhs.1995.494215
G. L. F. d. Silva, T. L. A. Valente, A. C. Silva, A. C. de Paiva, and M. Gattass, Convolutional neural network-based pso for lung nodule false positive reduction on ct images, Computer methods and programs in biomedicine, vol. 162, pp. 109 - 118, 2018. DOI: 10.1016/j.cmpb.2018.05.006
L. R. Dice, Measures of the amount of ecologic association between species, Ecology, vol. 26, no. 3, pp. 297 - 302, 1945. DOI: 10.2307/1932409
M. A. Ponti and G. B. P. da Costa, Como funciona o deep learning, arXiv preprint arXiv: 07908, 2018.
Publicado
09/09/2019
Como Citar
CIPRIANO, Carolina L. S.; DA SILVA, Giovanni L. F.; FERREIRA, Jonnison L.; SILVA, Aristófanes C.; DE PAIVA, Anselmo Cardoso.
Classification of brain lesions on magnetic resonance imaging using superpixel, PSO and convolutional neural network. In: WORKSHOP DE VISÃO COMPUTACIONAL (WVC), 15. , 2019, São Bernardo do Campo.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 126-130.
DOI: https://doi.org/10.5753/wvc.2019.7640.
