CLC number:
On-line Access: 2022-11-15
Received: 2021-12-13
Revision Accepted: 2022-05-03
Crosschecked: 2022-11-16
Cited: 0
Clicked: 1005
Citations: Bibtex RefMan EndNote GB/T7714
https://orcid.org/0000-0001-7825-4455
Dachuan GAO, Xiaodan YE, Xuewen HOU, Yang CHEN, Xue KONG, Yuanzhong XIE, Shengdong NIE. A method for distinguishing benign and malignant pulmonary nodules based on 3D dual path network aided by K-means clustering analysis[J]. Journal of Zhejiang University Science B,in press.Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/jzus.B2101009 @article{title="A method for distinguishing benign and malignant pulmonary nodules based on 3D dual path network aided by K-means clustering analysis", %0 Journal Article TY - JOUR
基于三维双路径网络与K均值聚类算法的肺结节良恶性鉴别方法1上海理工大学健康科学与工程学院,中国上海市,200093 2上海市胸科医院放射科,中国上海市,200030 3泰安中心医院医学影像中心,中国泰安市,271000 目的:为了提高肺癌早期诊断的准确性,本文使用机器学习,可以有效地帮助放射科医生区分肺结节的良恶性。 创新点:基于三维双路径网络(3DDPN)辅助K均值聚类分析区分良恶性肺结节,类别分析可以有效地表示良恶性肺结节的多种潜在亚型。 方法:在这项研究中,我们提出了一种基于3DDPN并辅以聚类分析来识别良恶性肺结节的新分类方案。首先,根据四位放射科医生的标注结果,从计算机断层扫描(CT)图像中截取以肺结节为中心,尺寸为64×64×64的像素单元;并训练pre-3D DPN模型提取卷积神经网络(CNN)特征。随后,采用随机森林特征选择算法滤除不相关的特征,并采用K均值聚类算法生成聚类标签。最后,使用具有新聚类标签的数据训练3D DPN对肺结节进行良恶性分类。 结果:使用肺影像数据联盟-影像数据库资源计划(LIDC-IDRI)数据库中的966个肺结节进行实验验证,最终所提方法的分类准确率、敏感度、特异度及接受者操作特性曲线(ROC)下面积(AUC)分别达92.86%、94.44%、91.94%及96.43%。此外,从上海胸科医院(SCH)收集了67个结节进行临床验证,获得的准确率为86.57%。 结论:本文所提出的方法可以准确地区分良恶性结节,可作为肺结节良恶性诊断的计算机辅助方法。 关键词组: Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article
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