Journal
IEEE ACCESS
Volume 7, Issue -, Pages 105146-105158Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2892795
Keywords
Mass detection; computer-aided diagnosis; deep learning; fusion feature; extreme learning machine
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Funding
- National Natural Science Foundation of China [61472069, 61402089, U1401256]
- China Postdoctoral Science Foundation [2018M641705]
- Fundamental Research Funds for the Central Universities [N161602003, N161904001, N160601001]
- Open Program of Neusoft Research of Intelligent Healthcare Technology, Co. Ltd. [NRIHTOP1802]
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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.
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