基于改进的Mask R-CNN的乳腺肿瘤目标检测研究

乳腺癌是全球女性死亡率最高的恶性肿瘤之一,早期发现有助于提升患者的存活率。本文利用深度学习中的目标检测网络对乳腺X线图像中的肿瘤病变区域进行定位和分类;然后选取Mask R-CNN网络作为目标检测模型,对Mask R-CNN的基准网络D-ShuffleNet进行改进,提出了一种新的网络——Mask R-CNN-II网络,并在Mask R-CNN-II网络中应用迁移学习算法。通过实验验证了Mask R-CNN-II网络比Mask R-CNN网络的检测精度更高,而且验证了所提基准网络、所使用的融合图像的思想以及迁移学习算法是有效的。Mask R-CNN-II有利于提高乳腺肿瘤的定位与分类,可为放射科医生提供辅助诊断意见,具有一定的临床应用价值。

Study on Target Detection of Breast Tumor Based on Improved Mask R-CNN

Breast cancer is one of the malignant tumors with the highest mortality among women globally and its early detection helps to increase the survival rate of patients.In this paper,we mainly used the target detection network in deep learning to locate and classify tumor lesion areas in the X-ray mammography images.Then,the Mask R-CNN network was taken as the target detection model for the improvement of its benchmark network D-ShuffleNet.Furthermore,a new network Mask R-CNN-II was proposed,to which the transfer learning algorithm was applied.Finally,it was experimentally demonstrated that the Mask R-CNN-II network had higher detection accuracy than the Mask R-CNN network.Besides,we also found that the proposed benchmark network,the idea of image fusion applied,and the transfer learning algorithm were effective.In conclusion,the network proposed in this paper is beneficial to improve the localization and classification of breast tumors and can provide auxiliary diagnostic advice for radiologists,which has certain clinical application value.