A Methodology for Tumor Detection in MRI using a New q-Gabor Function as a Convolutional Filter
Resumo
Convolutional Neural Networks (CNN) can achieve excellent computer-assisted diagnosis with a good amount of data. However, there is still a growing demand for specific data and information for training Machine Learning models, either for classification or other tasks such as segmentation. Towards this, the Data Augmentation (DA) technique can handle the small medical imaging dataset problem by generating artificial training data. In this context, Generative Adversarial Networks (GANs) can synthesize realistic images to increase the number of images in a dataset. Therefore, to maximize the DA efficiency in a CNNbased tumor classification task, we propose using non-extensive Gabor filters as a convolutional layer kernel initializer. Our proposal has been tested in the BraTS15 dataset and results show that CNN with an additional q-Gabor layer can achieve an average accuracy 3.65% better than CNN with Gabor and 5.03% better than the default model when trained with artificial images (data augmentation).
Palavras-chave:
data augmentation, GANs, synthetic medical imaging, gabor function, tumor detection
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J. Ker, L. Wang, J. P. Rao, and T. Lim, “Deep learning applications in medical image analysis,” IEEE Access, vol. 6, pp. 9375-9389, 2018.
P. K. Chahal, S. Pandey, and S. Goel, “A survey on brain tumor detection techniques for mr images,” Multimedia Tools and Applications, pp. 1- 44, 2020.
F. Perez, C. N. Vasconcelos, S. Avila, and E. Valle, “Data augmentation for skin lesion analysis,” in OR 2.0/CARE/CLIP/ISIC@MICCAI, 2018.
C. N. Vasconcelos and B. N. Vasconcelos, “Experiments using deep learning for dermoscopy image analysis,” Pattern Recognit. Lett., vol. 139, pp. 95-103, 2020.
Y. Liu, S. Stojadinovic, B. A. Hrycushko, Z. Wardak, S. K. M. Lau, W. Lu, Y. Yan, S. B. Jiang, X. Zhen, R. Timmerman, L. A. Nedzi, and X. Gu, “A deep convolutional neural network-based automatic delineation strategy for multiple brain metastases stereotactic radiosurgery,” PLoS ONE, vol. 12, 2017.
X. Yi, E. Walia, and P. S. Babyn, “Generative adversarial network in medical imaging: A review,” Medical image analysis, vol. 58, p. 101552, 2019.
M. Frid-Adar, I. Diamant, E. Klang, M. M. Amitai, J. Goldberger, and H. Greenspan, “Gan-based synthetic medical image augmentation for increased cnn performance in liver lesion classification,” Neurocomputing, vol. 321, pp. 321-331, 2018.
C. Han, L. Rundo, R. Araki, Y. Nagano, Y. Furukawa, G. Mauri, H. Nakayama, and H. Hayashi, “Combining noise-to-image and imageto-image gans: Brain MR image augmentation for tumor detection,” IEEE Access, vol. 7, pp. 156 966-156 977, 2019.
T. Karras, T. Aila, S. Laine, and J. Lehtinen, “Progressive growing of gans for improved quality, stability, and variation,” ArXiv, vol. abs/1710.10196, 2018.
C. Shorten and T. M. Khoshgoftaar, “A survey on image data augmentation for deep learning,” Journal of Big Data, vol. 6, pp. 1-48, 2019.
C. Nwankpa, W. L. Ijomah, A. Gachagan, and S. Marshall, “Activation functions: Comparison of trends in practice and research for deep learning,” ArXiv, vol. abs/1811.03378, 2018.
S. Sharma, S. Sharma, and A. Athaiya, “Activation functions in neural networks,” 2020.
L.-L. Huang, A. Shimizu, and H. Kobatake, “Classification-based face detection using gabor filter features,” Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings., pp. 397-402, 2004.
G. Maguolo, L. Nanni, and S. Ghidoni, “Ensemble of convolutional neural networks trained with different activation functions,” Expert Syst. Appl., vol. 166, p. 114048, 2021.
J. R. Movellan. Tutorial on gabor filters. [Online]. Available: https://inc.ucsd.edu/mplab/tutorials/gabor.pdf
C. Tsallis, “Nonextensive statistics: theoretical, experimental and computational evidences and connections,” Brazilian Journal of Physics, vol. 29, pp. 1-35, 1999.
__, “Nonextensive statistical mechanics and its applications,” 2001.
G. A. Giraldi, P. S. S. Rodrigues, L. S. Marturelli, and R. L. S. Silva, “Improving the initialization, convergence, and memory utilization for deformable models,” 2005.
P. S. S. Rodrigues, R.-F. Chang, and J. S. Suri, “Non-extensive entropy for cad systems of breast cancer images,” 2006 19th Brazilian Symposium on Computer Graphics and Image Processing, pp. 121-128, 2006.
G. A. Giraldi, P. S. S. Rodrigues, R. L. S. Silva, A. L. ApolinárioJr., and J. S. Suri, “Level set formulation for dual snake models,” 2007.
P. S. S. Rodrigues and G. A. Giraldi, “Improving the non-extensive medical image segmentation based on tsallis entropy,” Pattern Analysis and Applications, vol. 14, pp. 369-379, 2011.
L. Boltzmann, S. G. Brush, and N. L. Balazs, “Lectures on gas theory,” Physics Today, vol. 17, pp. 68-68, 1964.
P. S. S. Rodrigues, G. A. Wachs-Lopes, R. M. Santos, E. Coltri, and G. A. Giraldi, “A q-extension of sigmoid functions and the application for enhancement of ultrasound images,” Entropy, vol. 21, 2019.
M. et al., “The multimodal brain tumor image segmentation benchmark (brats),” IEEE Transactions on Medical Imaging, p. 33, 2014. [Online]. Available: https://hal.inria.fr/hal-00935640
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. C. Courville, and Y. Bengio, “Generative adversarial networks,” ArXiv, vol. abs/1406.2661, 2014.
K. He, X. Zhang, S. Ren, and J. Sun, “Identity mappings in deep residual networks,” ArXiv, vol. abs/1603.05027, 2016.
__, “Deep residual learning for image recognition,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770-778, 2016.
V. Nair and G. E. Hinton, “Rectified linear units improve restricted boltzmann machines,” in ICML, 2010.
L. Bottou, “Large-scale machine learning with stochastic gradient descent,” in COMPSTAT, 2010.
A. S. Alekseev and A. Bobe, “Gabornet: Gabor filters with learnable parameters in deep convolutional neural network,” 2019 International Conference on Engineering and Telecommunication (EnT), pp. 1-4, 2019.
Publicado
22/11/2021
Como Citar
SILVA, Vinicius de A.; LAHERAS, Lucas P.; ACCHETTA, Everton C.; RODRIGUES, Paulo S..
A Methodology for Tumor Detection in MRI using a New q-Gabor Function as a Convolutional Filter. In: WORKSHOP DE VISÃO COMPUTACIONAL (WVC), 17. , 2021, Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 165-170.
DOI: https://doi.org/10.5753/wvc.2021.18908.
