Mammographic mass detection based on convolution neural network | IEEE Conference Publication | IEEE Xplore

Mammographic mass detection based on convolution neural network


Abstract:

Mammography is one of the broadly used imaging modality for breast cancer screening and detection. Locating mass from the whole breast is an important work in computer-ai...Show More

Abstract:

Mammography is one of the broadly used imaging modality for breast cancer screening and detection. Locating mass from the whole breast is an important work in computer-aided detection. Traditionally, handcrafted features are employed to capture the difference between a mass region and a normal region. Recently convolution neural network (CNN) which automatically discovers features from the images shows promising results in many pattern recognition tasks. In this paper, three mass detection schemes based on CNN are evaluated. First, a suspicious region locating method based on heuristic knowledge is employed. Then three different CNN schemes are designed to classify the suspicious region as mass or normal. The proposed schemes are evaluated on a dataset of 352 mammograms. Compared with several handcrafted features, CNN-based methods shows better mass detection performance in terms of free receiver operating characteristic (FROC) curve.
Date of Conference: 20-24 August 2018
Date Added to IEEE Xplore: 29 November 2018
ISBN Information:
Print on Demand(PoD) ISSN: 1051-4651
Conference Location: Beijing, China

References

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