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

IEEE Access

卷:7

Hao Zhang¹  Huaxia Wang²  Yudong Yao²  Hanyu Jiang²  Junchang Xin³  Mo Li³  Zhiqiong Wang⁴ 

[1] Department of Breast Surgery, Shengjing Hospital of China Medical University, Shenyang, China;

[2] Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, USA;

[3] Key Laboratory of Big Data Management and Analytics (Liaoning), School of Computer Science and Engineering, Northeastern University, Shenyang, China;

[4] Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, China;

关键词: Mass detection;    computer-aided diagnosis;    deep learning;    fusion feature;    extreme learning machine;   

DOI  :  10.1109/ACCESS.2019.2892795

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown