An Automatic Convolution Neural Network-Based Framework for Robust Classification of Breast Cancer Histopathological Images
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-16-8364-0_20
Reference15 articles.
1. Alkhaleefah, M., & Wu, C. C. (2019). A hybrid CNN and RBF-based SVM approach for breast cancer classification in mammograms. In Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018, pp. 894–899.
2. Alqudah, A., & Alqudah, A. M. (2019). Sliding window based support vector machine system for classification of breast cancer using histopathological microscopic images. IETE Journal of Research, 1–9.
3. Araujo, T., Aresta, G., Castro, E., Rouco, J., Aguiar, P., Eloy, C., & Campilho, A. (2017). Classification of breast cancer histology images using convolutional neural networks. PLoS ONE, 12(6), 216–230.
4. Bandyopadhyay, S., & Maulik, U. (2002). Genetic clustering for automatic evolution of clusters and application to image classification. Pattern Recognition, 35(6), 1197–1208.
5. Bayramoglu, N., Kannala, J., & Heikkila, J. (2016). Deep learning for magnification independent breast cancer histopathology image classification. In Proceedings - International Conference on Pattern Recognition, 0, pp. 2440–2445.
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