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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2018, Vol. 31 
