Abstract
The convolutional neural network has achieved great success in the classification of medical imaging including breast cancer classification. Breast cancer is one of the most dangerous cancers impacting women all over the world. In this paper, we propose a deep learning framework. This framework includes the proposed pre-processing phase and the proposed separable convolutional neural network (SCNN) model. Our pre-processing uses patch extraction and data augmentation to enrich the training set and improve the performance. The SCNN model uses separable convolution and parametric rectified linear unit (PRELU) as an activation function. The SCNN shows superior performance and faster than the pre-trained neural network models. The SCNN approach is evaluated using the BACH2018 dataset [1]. We test the performance using 40 random images. The framework achieves accuracy between 97.5% and 100%. The best accuracy is 100% for multi-class and binary class. The framework provides superior classification performance compared to existing approaches.
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Gaber, H., Mohamed, H., Ibrahim, M. (2021). Breast Cancer Classification from Histopathological Images with Separable Convolutional Neural Network and Parametric Rectified Linear Unit. In: Hassanien, A.E., Slowik, A., Snášel, V., El-Deeb, H., Tolba, F.M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2020. AISI 2020. Advances in Intelligent Systems and Computing, vol 1261. Springer, Cham. https://doi.org/10.1007/978-3-030-58669-0_34
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DOI: https://doi.org/10.1007/978-3-030-58669-0_34
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