[1]孙娇娇,龚 安,史海涛.基于卷积神经网络的低剂量 CT 图像肺结节检测[J].计算机技术与发展,2019,29(11):173-177.[doi:10. 3969 / j. issn. 1673-629X. 2019. 11. 035]
　SUN Jiao-jiao,GONG An,SHI Hai-tao.Detection of Low-dose CT Pulmonary Nodule Based on Convolutional Neural Network[J].,2019,29(11):173-177.[doi:10. 3969 / j. issn. 1673-629X. 2019. 11. 035]
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基于卷积神经网络的低剂量 CT 图像肺结节检测()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
29
期数:
2019年11期
页码:
173-177
栏目:
应用开发研究
出版日期:
2019-11-10

文章信息/Info

Title:
Detection of Low-dose CT Pulmonary Nodule Based on Convolutional Neural Network
文章编号:
1673-629X(2019)11-0173-05
作者:
孙娇娇; 龚 安; 史海涛
中国石油大学(华东) 计算机与通信工程学院,山东 青岛 266580
Author(s):
SUN Jiao-jiao; GONG An; SHI Hai-tao
School of Computer &Communication Engineering,China University of Petroleum,Qingdao 266580,China
关键词:
图像识别; 肺结节检测; 卷积神经网络; CU-net; CFaster-Rcnn; 深度学习
Keywords:
image identification; pulmonary nodule detection; convolution neural network; CU-net; CFaster-Rcnn; deep learning
分类号:
TP391
DOI:
10. 3969 / j. issn. 1673-629X. 2019. 11. 035
摘要:
由于现有的肺结节检测方法大多局限于形态特征等主观因素所带来的影响,准确率有待提升且假阳性高,而目前的深度学习检测方法较基础,虽然有效降低了假阳率,但准确率也会随之下降。 文中提出了一种结合改进全卷积神经网络(CU-net)和循环 3D Faster-Rcnn(3D CFaster-Rcnn)的肺结节检测方法来解决这一问题。 该方法使用 CU-net 对 CT 图像进行候选区域检测,快速定位出图像的疑似结节区域,输出的图像尺寸不变,通过疑似区域坐标计算,提取候选区域三维立体像素块在 3D CFaster-Rcnn 模型中进行训练,进行假阳性去除。 候选区域检测步骤结节召回率为 98.5%,在进行假阳性处理即模型优化后,在假阳率为 1.65 时得到了 92.6%的准确率。 与其他方法的对比结果表明,该模型在假阳性较低时取得了较高的准确率。
Abstract:
Since the existing lung nodule detection methods are mostly limited to subjective factors such as morphological characteristics,the accuracy needs to be improved with high false positive. The existing deep learning detection method is primary. Although the false positive rate can be effectively reduced,the accuracy rate will also decrease. To solve the defect,we propose a lung nodule detection method which combines the improved whole convolutional neural network (CU-net) and the cyclic 3D Faster-Rcnn (3D CFaster-Rcnn). The CU-net is used to perform candidate region detection on CT images,and then suspected nodule regions of the images are quickly located without changing size of the images. The candidate area three-dimensional pixel block is extracted and trained in the 3D CFaster-Rcnn model by pseudo-region coordinate calculation,as well,the false positive removal is operated. It is obtained that the nodule recall rate of the candidate region detection step is 98.5%. After the model optimization to false positive treatment, the accuracy rate achieves 92.6% with false positive rate being 1. 65. Compared with other methods,the model is more accurate when the false positives are lower.

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更新日期/Last Update: 2019-11-10