Breast cancer classification using deep belief networks

作者：

Highlights：

• We present a CAD scheme using DBN unsupervised path followed by NN supervised path.

• Our two-phase method ‘DBN-NN’ classification accuracy is higher than using one phase.

• Overall accuracy of DBN-NN reaches 99.68% with 100% sensitivity & 99.47% specificity.

• DBN-NN was tested on the Wisconsin Breast Cancer Dataset (WBCD).

• DBN-NN results show classifier performance improvements over previous studies.

摘要

•We present a CAD scheme using DBN unsupervised path followed by NN supervised path.•Our two-phase method ‘DBN-NN’ classification accuracy is higher than using one phase.•Overall accuracy of DBN-NN reaches 99.68% with 100% sensitivity & 99.47% specificity.•DBN-NN was tested on the Wisconsin Breast Cancer Dataset (WBCD).•DBN-NN results show classifier performance improvements over previous studies.

论文关键词：Breast cancer diagnosis,CAD,Classification,Deep learning based classifier,Pattern recognition

论文评审过程：Received 14 February 2015, Revised 14 October 2015, Accepted 15 October 2015, Available online 27 October 2015, Version of Record 18 November 2015.

论文官网地址：https://doi.org/10.1016/j.eswa.2015.10.015