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This research presents a breast cancer detection system using deep learning method. Breast cancer detection in a large slide of biopsy image are hard task because it needs manual observation by pathologist to find the malignant region. Deep learning model used in this research are made up of multiple layer of residual convolutional neural network and instead of using other type of classifier, multilayer neural network were used as the classifier and stacked together and trained using end-to-end training approach. The system are trained using invasive ductal carcinoma dataset from Hospital of the University of Pennsylvania and The Cancer Institute of New Jersey. From this dataset, 80% and 20% were randomly sampled and used as training and testing data respectively. Training a neural network on an imbalanced dataset are quite challenging. Weighted loss function were used as the objective function to tackle this problem. We achieve 78.26% and 78.03% for Recall and F1-Score metrics respectively which are an improvement compared to the previous approach.
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 BreastCancer.Org (2018), Invasive Ductal Carcinoma (IDC). Available : https://www.breastcancer.org/symptoms/types/idc
 A. Cruz-Roa, A. Basavanhally, F. Gonz’alez, H. Gilmore, M. Feldman, S. Ganesan, N. Shih, J. Tomaszewski, and A. Madabhushi, “Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks,” in Medical Imaging 2014: Digital Pathology. International Society for Optics and Photonics, 2014, vol. 9041, p. 904103.
 A. Janowczyk and A. Madabhushi, “Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases,” Journal of pathology informatics, vol. 7, 2016.
 Vinod Nair and Geoffrey E. Hinton. “Rectified linear units improve restricted boltzmann machines” ICML'10 Proceedings of the 27th International Conference on International Conference on Machine Learning. Pages 807-814.
 Y. Bengio, “Practical recommendations for gradientbased training of deep architectures,” in Neural networks: Tricks of the trade, pp. 437–478. Springer, 2012.
 P.D. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980, 2014.
 Andrew L. Maas, Awni Y. Hannun, Andrew Y. Ng. “Rectifier Nonlinearities Improve Neural Network Acoustic Models”
 Duchi J, Hazan E, Singer Y. Adaptive subgradient methods for online learning and stochastic optimization. Journal of Machine Learning Research. 2011;12:2121-59.
 A. Krizhevsky, I. Sutskever, and E.G. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems, 2012, pp. 1097–1105.
 K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770–778.
 Doyle, S., Agner, S., Madabhushi, A., Feldman, M., and Tomaszewski, J., (2008). “Automated grading of breast cancer histopathology using spectral clustering with textural and architectural image features,”
 Dundar, M. M., Badve, S., Bilgin, G., Raykar, V., Jain, R., Sertel, O., and Gurcan, M. N., (2011). “Computerized classification of intraductal breast lesions using histopathological images,”
 Niwas, S. I., Palanisamy, P., Zhang, W., Mat Isa, N. A., and Chibbar, R., (2011). “Log-gabor wavelets based breast carcinoma classification using least square support vector machine.”
 M. Rohmatillah, S. H. Pramono, Rahmadwati, H. Suyono and S. A. Sena, "Automatic Cervical Cell Classification Using Features Extracted by Convolutional Neural Network," 2018 Electrical Power, Electronics, Communications, Controls and Informatics Seminar (EECCIS), Batu, East Java, Indonesia, 2018, pp. 382-386