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Breast tumor detection in double views mammography based on extreme learning machine

  • Extreme Learning Machine and Applications
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Abstract

Mammography is one of the most important methods for breast tumor detection, while existing computer-aided diagnosis (CAD) technology based on single-view mammograms ignores the contrastive feature between medio-lateral oblique (MLO) and cranio-caudal (CC) views, and CAD technology based on double-view overlooks features of single views. But in clinical environment, radiologists not only read both CC view images and MLO view images individually, but also contrast these two types of views to diagnose each case. Therefore, to simulate diagnosis process of radiologists, in this paper, a fused feature model which blends features of single views with contrastive features of double views is proposed. The fused feature model is optimized by means of feature selection methods. Then, a CAD detection method based on extreme learning machine, a classifier with wonderful universal approximation capability, is proposed to improve the effectiveness of breast tumor detection by applying the optimum fused feature. The effectiveness of proposed method is verified by 222 pairs of mammograms from 222 women in Northeast China through the complete experiment.

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Acknowledgments

This research was partially supported by the National Natural Science Foundation of China under Grant Nos. 61402089, 61100022, 61472069, and the National High Technology Research and Development Plan (863 Plan) under Grant No. 2012AA02A606. The authors wish to express their sincere appreciation to Zhongzhou Chen, Shiya Zhang and Sheng Gu for revising and polishing.

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Authors and Affiliations

  1. Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, China

    Zhiqiong Wang, Qixun Qu & Yan Kang

  2. Key Laboratory of Medical Image Computing, Ministry of Education, Shenyang, China

    Zhiqiong Wang

  3. College of Information Science and Engineering, Northeastern Univer sity, Shenyang, China

    Zhiqiong Wang & Ge Yu

Authors
  1. Zhiqiong Wang
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  2. Qixun Qu
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  3. Ge Yu
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  4. Yan Kang
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Correspondence to Yan Kang.

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Wang, Z., Qu, Q., Yu, G. et al. Breast tumor detection in double views mammography based on extreme learning machine. Neural Comput & Applic 27, 227–240 (2016). https://doi.org/10.1007/s00521-014-1764-0

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  • DOI: https://doi.org/10.1007/s00521-014-1764-0

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