Diagnóstico automático de lesões malignas de mama em imagens histopatológicas usando mensuração mútua de características de textura bioinspiradas e Deep Learning
Resumo
O câncer de mama é uma doença resultante da multiplicação anormal de células da mama, que formam massas. O diagnóstico precoce da malignidade do câncer de mama é fundamental para a sobrevivência do paciente. O Processamento Digital de Imagens em conjunto com técnicas computacionais de aprendizado de máquina possibilitam a criação de métodos para detecção da malignidade de tumores em imagens de exames histopatológicos. Assim, este artigo apresenta uma metodologia para o diagnóstico automático de lesões malignas de mama em imagens histopatológicas baseada na mensuração mútua de características de textura bioinspiradas e Deep Learning Features. Os resultados obtidos indicam que o método é promissor, alcançando acurácia de 92,9% com o classificador Random Forest.
Referências
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Spanhol, F. A., Oliveira, L. S., Petitjean, C., and Heutte, L. (2016). Breast cancer histopathological image classification using convolutional neural networks. In 2016 international joint conference on neural networks (IJCNN), pages 2560-2567. IEEE.
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2818-2826.
Tsochatzidis, L., Zagoris, K., Arikidis, N., Karahaliou, A., Costaridou, L., and Pratikakis, I. (2017). Computer-aided diagnosis of mammographic masses based on a supervised content-based image retrieval approach. Pattern Recognition, 71:106-117.
Almaraz-Damian, J.-A., Ponomaryov, V., Sadovnychiy, S., and Castillejos-Fernandez, H. (2020). Melanoma and nevus skin lesion classification using handcraft and deep learning feature fusion via mutual information measures. Entropy, 22(4):484.
Alom, M. Z., Yakopcic, C., Nasrin, M. S., Taha, T. M., and Asari, V. K. (2019). Breast cancer classification from histopathological images with inception recurrent residual convolutional neural network. Journal of digital imaging, 32(4):605-617.
Araújo, F. H., Santana, A. M., and Neto, P. d. A. S. (2016). Using machine learning to support healthcare professionals in making preauthorisation decisions. International journal of medical informatics, 94:1-7.
Araujo, F. H., Silva, R. R., Medeiros, F. N., Parkinson, D. D., Hexemer, A., Carneiro, C. M., and Ushizima, D. M. (2018). Reverse image search for scientific data within and beyond the visible spectrum. Expert Systems with Applications, 109:35-48.
Ataky, S. T. M. and Koerich, A. L. (2022). A novel bio-inspired texture descriptor based on biodiversity and taxonomic measures. Pattern Recognition, 123:108382.
Azzoli, C. G., Baker Jr, S., Temin, S., Pao, W., Aliff, T., Brahmer, J., Johnson, D. H., Laskin, J. L., Masters, G., Milton, D., et al. (2009). American society of clinical oncology clinical practice guideline update on chemotherapy for stage iv non-small-cell lung cancer. Journal of clinical oncology, 27(36):6251.
Bayramoglu, N., Kannala, J., and Heikkilä, J. (2016). Deep learning for magnification independent breast cancer histopathology image classification. In 2016 23rd International conference on pattern recognition (ICPR), pages 2440-2445. IEEE.
Bisneto, T. R. V., de Carvalho Filho, A. O., and Magalhães, D. M. V. (2020). Generative adversarial network and texture features applied to automatic glaucoma detection. Applied Soft Computing, 90:106165.
Buitinck, L., Louppe, G., Blondel, M., Pedregosa, F., Mueller, A., Grisel, O., Niculae, V., Prettenhofer, P., Gramfort, A., Grobler, J., Layton, R., VanderPlas, J., Joly, A., Holt, B., and Varoquaux, G. (2013). API design for machine learning software: experiences from the scikit-learn project. In ECML PKDD Workshop: Languages for Data Mining and Machine Learning, pages 108-122.
Carvalho, E. D., Antonio Filho, O., Silva, R. R., Araujo, F. H., Diniz, J. O., Silva, A. C., Paiva, A. C., and Gattass, M. (2020a). Breast cancer diagnosis from histopathological images using textural features and cbir. Artificial intelligence in medicine, 105:101845.
Carvalho, E. D., de Carvalho Filho, A. O., de Sousa, A. D., Silva, A. C., and Gattass, M. (2018). Method of differentiation of benign and malignant masses in digital mammograms using texture analysis based on phylogenetic diversity. Computers & Electrical Engineering, 67:210-222.
Carvalho, E. D., Filho, A. O., Silva, R. R., Araujo, F. H., Diniz, J. O., Silva, A. C., Paiva, A. C., and Gattass, M. (2020b). Breast cancer diagnosis from histopathological images using textural features and cbir. Artificial Intelligence in Medicine, 105:101845.
Chawla, N. V., Bowyer, K. W., Hall, L. O., and Kegelmeyer, W. P. (2002). Smote: synthetic minority over-sampling technique. Journal of artificial intelligence research, 16:321-357.
Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1251-1258.
Erfankhah, H., Yazdi, M., Babaie, M., and Tizhoosh, H. R. (2019). Heterogeneity-aware local binary patterns for retrieval of histopathology images. IEEE Access, 7:18354-18367.
Han, Z., Wei, B., Zheng, Y., Yin, Y., Li, K., and Li, S. (2017). Breast cancer multiclassification from histopathological images with structured deep learning model. Scientific reports, 7(1):1-10.
He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778.
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861.
Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4700-4708.
Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25.
Luz, D. S., Lima, T. J., Silva, R. R., Magalhaes, D. M., and Araujo, F. H. (2022). Automatic detection metastasis in breast histopathological images based on ensemble learning and color adjustment. Biomedical Signal Processing and Control, 75:103564.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015). Imagenet large scale visual recognition challenge. International journal of computer vision, 115(3):211-252.
Simonyan, K. and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
Spanhol, F. A., Oliveira, L. S., Petitjean, C., and Heutte, L. (2015). A dataset for breast cancer histopathological image classification. Ieee transactions on biomedical engineering, 63(7):1455-1462.
Spanhol, F. A., Oliveira, L. S., Petitjean, C., and Heutte, L. (2016). Breast cancer histopathological image classification using convolutional neural networks. In 2016 international joint conference on neural networks (IJCNN), pages 2560-2567. IEEE.
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2818-2826.
Tsochatzidis, L., Zagoris, K., Arikidis, N., Karahaliou, A., Costaridou, L., and Pratikakis, I. (2017). Computer-aided diagnosis of mammographic masses based on a supervised content-based image retrieval approach. Pattern Recognition, 71:106-117.
Publicado
07/06/2022
Como Citar
BISNETO, Tomaz Ribeiro Viana; ARAÚJO, Flávio Henrique Duarte de.
Diagnóstico automático de lesões malignas de mama em imagens histopatológicas usando mensuração mútua de características de textura bioinspiradas e Deep Learning. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 22. , 2022, Teresina.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 246-255.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2022.222605.
