Breast Cancer Detection Using Extreme Learning Machine Based on Feature Fusion with CNN Deep Features

Zhiqiong Wang, Mo Li, Huaxia Wang, Hanyu Jiang, Yudong Yao, Hao Zhang, Junchang Xin

Research output: Contribution to journalArticlepeer-review

264 Scopus citations

Abstract

A computer-aided diagnosis (CAD) system based on mammograms enables early breast cancer detection, diagnosis, and treatment. However, the accuracy of the existing CAD systems remains unsatisfactory. This paper explores a breast CAD method based on feature fusion with convolutional neural network (CNN) deep features. First, we propose a mass detection method based on CNN deep features and unsupervised extreme learning machine (ELM) clustering. Second, we build a feature set fusing deep features, morphological features, texture features, and density features. Third, an ELM classifier is developed using the fused feature set to classify benign and malignant breast masses. Extensive experiments demonstrate the accuracy and efficiency of our proposed mass detection and breast cancer classification method.

Original languageEnglish
Article number8613773
Pages (from-to)105146-105158
Number of pages13
JournalIEEE Access
Volume7
DOIs
StatePublished - 2019

Keywords

  • Mass detection
  • computer-aided diagnosis
  • deep learning
  • extreme learning machine
  • fusion feature

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