Computer-aided detection and classification of microcalcification clusters on full field digital mammograms using deep convolution neural network

Guanxiong Cai; Yanhui Guo; Weiguo Chen; Hui Zeng; Yuanpin Zhou; Yao Lu

doi:10.1117/12.2512355

13 March 2019 Computer-aided detection and classification of microcalcification clusters on full field digital mammograms using deep convolution neural network

Guanxiong Cai, Yanhui Guo, Weiguo Chen, Hui Zeng, Yuanpin Zhou, Yao Lu

Author Affiliations +

Proceedings Volume 10950, Medical Imaging 2019: Computer-Aided Diagnosis; 109502J (2019) https://doi.org/10.1117/12.2512355
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

Breast cancer is presently one of the most common cancer among women and has high morbidity and mortality worldwide. The emergence of microcalcifications (MCs) is an important early sign of breast cancer. In this study, a computer-aided detection and diagnosis (CAD) system is developed to automatically detect MC clusters (MCCs) and further providing cancer likelihood prediction. Firstly, each individual MC is detected using our previously designed MC detection system, which includes preprocessing, MC enhancement, MC candidate detection, false positive (FP) reduction of MCs and regional clustering procedures. Secondly, a deep convolution neural network (DCNN) is trained on 394 clinical high-resolution full field digital mammograms (FFDMs) containing biopsy-proven MCCs to discriminate MCC lesions. For cluster-based detection evaluation, a 90% sensitivity is obtained with a FP rate of 0.2 FPs per image. The classification performance of the whole system is validated on 70 cases and tested on 71 cases, and for case-based diagnosis evaluation, the area under the receiver operating characteristic curve (AUC) on validation and testing sets are 0.945 and 0.932, respectively. Different from previous literatures committing to finding and selecting effective features, the proposed method replaces manual feature extraction step by using deep convolution neural network. The obtained results demonstrate that the proposed method is effective in the automatically detection and classification of MCCs.

Citation Download Citation

Guanxiong Cai, Yanhui Guo, Weiguo Chen, Hui Zeng, Yuanpin Zhou, and Yao Lu "Computer-aided detection and classification of microcalcification clusters on full field digital mammograms using deep convolution neural network", Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109502J (13 March 2019); https://doi.org/10.1117/12.2512355

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KEYWORDS

Mammography

Breast cancer

Convolution

Image processing

Neural networks

Breast

Classification systems

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