Multi-View Auxiliary Diagnosis Algorithm for Lung Nodules | |
Qiu, Shi1; Li, Bin2; Zhou, Tao3; Li, Feng4; Liang, Ting5 | |
作者部门 | 光谱成像技术研究室 |
2022 | |
发表期刊 | Computers, Materials and Continua |
ISSN | 15462218;15462226 |
卷号 | 72期号:3页码:4897-4910 |
产权排序 | 1 |
摘要 | Lung is an important organ of human body. More and more people are suffering from lung diseases due to air pollution. These diseases are usually highly infectious. Such as lung tuberculosis, novel coronavirus COVID-19, etc. Lung nodule is a kind of high-density globular lesion in the lung. Physicians need to spend a lot of time and energy to observe the computed tomography image sequences to make a diagnosis, which is inefficient. For this reason, the use of computer-assisted diagnosis of lung nodules has become the current main trend. In the process of computer-aided diagnosis, how to reduce the false positive rate while ensuring a low missed detection rate is a difficulty and focus of current research. To solve this problem, we propose a three-dimensional optimization model to achieve the extraction of suspected regions, improve the traditional deep belief network, and to modify the dispersion matrix between classes. We construct a multi-view model, fuse local three-dimensional information into two-dimensional images, and thereby to reduce the complexity of the algorithm. And alleviate the problem of unbalanced training caused by only a small number of positive samples. Experiments show that the false positive rate of the algorithm proposed in this paper is as low as 12%, which is in line with clinical application standards. © 2022 Tech Science Press. All rights reserved. |
关键词 | Lung nodules deep belief network computer-aided diagnosis multi-view |
DOI | 10.32604/cmc.2022.026855 |
收录类别 | SCI ; EI |
语种 | 英语 |
WOS记录号 | WOS:000799234000004 |
出版者 | Tech Science Press |
EI入藏号 | 20221712035711 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.opt.ac.cn/handle/181661/95856 |
专题 | 光谱成像技术研究室 |
通讯作者 | Li, Bin |
作者单位 | 1.Key Laboratory of Spectral Imaging Technology CAS, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an; 710119, China; 2.School of Information Science and Technology, Northwest University, Xi'an; 710127, China; 3.School of Computer Science and Engineering, North Minzu University, Yinchuan; 750021, China; 4.Institute of Education, University College London, London, United Kingdom; 5.Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an; 10061, China |
推荐引用方式 GB/T 7714 | Qiu, Shi,Li, Bin,Zhou, Tao,et al. Multi-View Auxiliary Diagnosis Algorithm for Lung Nodules[J]. Computers, Materials and Continua,2022,72(3):4897-4910. |
APA | Qiu, Shi,Li, Bin,Zhou, Tao,Li, Feng,&Liang, Ting.(2022).Multi-View Auxiliary Diagnosis Algorithm for Lung Nodules.Computers, Materials and Continua,72(3),4897-4910. |
MLA | Qiu, Shi,et al."Multi-View Auxiliary Diagnosis Algorithm for Lung Nodules".Computers, Materials and Continua 72.3(2022):4897-4910. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Multi-View Auxiliary(926KB) | 期刊论文 | 出版稿 | 限制开放 | CC BY-NC-SA | 请求全文 |
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