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| Microcalcification Clusters Detection Method Based on Deep Learning and Multi-scale Feature Fusion |
| ZHANG Xinsheng1, WANG Zhe1 |
| 1.School of Management]Xi′an University of Architecture and Technology, Xi′an 710055 |
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Abstract To accurately identify the microcalcification in X-ray images for diagnosis and early prevention of breast cancer, a microcalcification object detection method combining fine-grained cascading enhancement-network(FCE-Net) and multi-scale feature fusion(MFF]is proposed. Firstly, the residual weights of FCE-Net are accumulated and the multi-branch structure is enhanced within the convolution module to obtain hierarchical and fine-grained convolution plots. Then, a candidate detection network based on MFF is constructed and multi-scale features are merged by double-upsampling. Thus, the confidence and regional coordinates of the object of the microcalcification clusters are acquired. Finally, the object region is classified and the bounding box is adjusted in the pooling layer of the region of interest. The experimental results on MIAS breast cancer dataset show that the method combining FCE-Net and MFF has a better ability to extract deep feature with enhanced classification and positioning accuracies.
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Received: 30 May 2018
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| Fund:Supported by General Program of National Natural Science Foun-dation of China(No.41877527), Natural Science Foundation of Shaanxi Province(No.2016JM6023) |
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Corresponding Authors:
ZHANG Xinsheng,Ph.D., professor. His research interests include pattern recognition and intelligent information processing.
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| About author:: WANG Zhe, master student. His research interests include pattern recognition and inte-lligent]information processing. |
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2018, Vol. 31 
