基于掩模R-CNN的乳腺癌病灶检测与分割

Hama Soltani, M. Amroune, Issam Bendib, M. Haouam
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引用次数: 2

摘要

乳腺癌一直困扰着所有女性。为了达到这一目的,建立一套诊断可疑肿块的系统是非常重要的。另一方面,这是一项艰巨的任务,因为乳房肿块的大小和外观各不相同。在本文中,我们提出了一种基于深度学习Mask RCNN模型的乳房质量自动分割方法。我们的模型使用INbreast的公共数据集进行训练和测试。该方法在INbreast数据集上的准确率为95.87,F1得分为81.05。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Breast Cancer Lesion Detection and Segmentation Based On Mask R-CNN
Breast cancer is an obsession that haunts all women. but early detection for it increases the cure rate, for attain this objective It is very important to create a system to diagnose suspicious masses. On the other hand, is a difficult task due to the fact that breast lumps vary in size and appearance. In this paper, we propose an automatic breast mass segmentation method based on the Mask RCNN model of deep learning using detectron2.our model is trained and testing using the public dataset INbreast. The proposed method achieved results with precision and F1 score 95.87 and 81.05 on INbreast dataset, respectively.
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