Detection and Segmentation of Breast Masses Based on Multi-Layer Feature Fusion

In breast mass detection, there are many different sizes of masses in the image. However, when the existing target detection model is directly used to detect the breast mass, it is easy to appear the phenomenon of misdetection and missed detection. Therefore, in order to improve the detection accuracy of breast masses, this paper proposed a target detection model D-Mask R-CNN based on Mask R-CNN, which is suitable for breast masses detection. Firstly, this paper improved the internal structure of FPN, and modified the lateral connection mode in the original FPN structure to dense connection. Secondly, modified the size of the anchor of RPN to improve the location accuracy of breast masses. Finally, Soft-NMS was used to replace the NMS in the original model to reduce the possibility that the correct prediction results may be eliminated during the NMS process. This paper used the CBIS-DDSM dataset for all experiments. The results showed that the mAP value of the improved model for detecting breast masses reached 0.66 in the test set, which was 0.05 higher than that of the original Mask R-CNN.

Automated summary

This paper proposes a target detection model D-Mask R-CNN based on Mask R-CNN for breast mass detection. Improvements were made to the internal structure of FPN and the size of RPN anchor. Soft-NMS was used to replace NMS for better detection accuracy. Experimental results showed that the improved model achieved a higher mAP value of 0.66 compared to the original Mask R-CNN.