Breast Cancer Detection using Residual Convolutional Neural Network and Weighted Loss

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Samuel Aji Sena Panca Mudjirahardjo Sholeh Hadi Pramono


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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SENA, Samuel Aji; MUDJIRAHARDJO, Panca; PRAMONO, Sholeh Hadi. Breast Cancer Detection using Residual Convolutional Neural Network and Weighted Loss. JURNAL INFOTEL, [S.l.], v. 11, n. 2, july 2019. ISSN 2460-0997. Available at: <>. Date accessed: 19 sep. 2019. doi:


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