Journal of health informatics and statistics

The Korean Society of Health Informatics and Statistics (한국보건정보통계학회)
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Breast Cancer Classification using Deep Learning-based Ensemble

딥러닝 기반 앙상블을 이용한 유방암 분류

  • Choi, Do-Yeon (Department of Statistics, Busan National University) ;
  • Jeong, Kwang-Mo (Department of Statistics, Busan National University) ;
  • Lim, Dong Hoon (Department of Information Statistics and RINS, Gyeongsang National University)
  • Received : 2018.02.25
  • Accepted : 2018.05.24
  • Published : 2018.05.31
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Abstract

Objectives: We propose a deep learning-based ensemble for improving breast cancer classification and compare it with existing six models including deep neural network on two UCI data. Methods: We propose a deep learning-based stacking ensemble method. We first applied five classifications methods individually, which were k-nearest neighbor, decision trees, support vector machines, discriminant analysis, and logistic regression analysis and then adopt a deep learning to the predictions derived from these methods after using 5-fold cross validation technique. We compared the proposed deep learning-based ensemble method with these methods for two UCI data through classification accuracy, ROC curves and c-statistics. Results: Experimental results for two UCI data showed that the proposed deep learning-based ensemble outperformed single k-nearest neighbor, decision trees, support vector machines discriminant analysis, and logistic regression analysis as well as deep neural network in terms of various performance measures. Conclusions: We proposed deep learning-based ensemble for improving breast cancer classification. The deep learning-based ensemble outperformed existing single models for all applications in terms of various performance measures.

Keywords

Acknowledgement

Supported by : National Research Foundation of Korea (NRF)

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