CASIA OpenIR  > 数字内容技术与服务研究中心  > 听觉模型与认知计算
Automated pulmonary nodule detection in CT images using deep convolutional neural networks
Xie, Hongtao1; Yang, Dongbao1,2,3; Sun, Nannan3; Chen, Zhineng4; Zhang, Yongdong1
Source PublicationPATTERN RECOGNITION
ISSN0031-3203
2019
Volume85Pages:109-119
Corresponding AuthorYang, Dongbao(yangdongbao0903@163.com)
AbstractLung cancer is one of the leading causes of cancer-related death worldwide. Early diagnosis can effectively reduce the mortality, and computer-aided diagnosis (CAD) as an important way to assist doctors has developed rapidly. In particular, automated pulmonary nodule detection in computed tomography (CT) images is crucial to CAD. It is a challenging task to quickly locate the exact positions of lung nodules. In this paper, a novel automated pulmonary nodule detection framework with 2D convolutional neural network (CNN) is proposed to assist the CT reading process. Firstly, we adjust the structure of Faster R-CNN with two region proposal networks and a deconvolutional layer to detect nodule candidates, and then three models are trained for three kinds of slices for later result fusion. Secondly, a boosting architecture based on 2D CNN is designed for false positive reduction, which is a classifier to distinguish true nodules from the candidates. The misclassified samples are still kept for retraining a model which boosts the sensitivity for pulmonary nodule detection. Finally, the results of these networks are fused to vote out the final classification results. Extensive experiments are conducted on LUNA16, and the sensitivity of nodule candidate detection achieves 86.42%. For the false positive reduction, the sensitivity reaches 73.4% and 74.4% at 1/8 and 1/4 FPs/scan, respectively. It illustrates that the proposed method can obviously achieve accurate pulmonary nodule detection. (C) 2018 Elsevier Ltd. All rights reserved.
KeywordNodule detection Convolutional neural network False positive reduction Computer-aided diagnosis
DOI10.1016/j.patcog.2018.07.031
WOS KeywordCOMPUTED-TOMOGRAPHY IMAGES ; FALSE-POSITIVE REDUCTION ; AIDED DETECTION ; LUNG NODULES ; CLASSIFICATION ; CNNS ; SEGMENTATION ; VALIDATION ; SEQUENCES ; FRAMEWORK
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program of China[2017YFC0820600] ; National Natural Science Foundation of China[61525206] ; National Natural Science Foundation of China[61771468] ; National Natural Science Foundation of China[61772526] ; Youth Innovation Promotion Association Chinese Academy of Sciences[2017209]
Funding OrganizationNational Key Research and Development Program of China ; National Natural Science Foundation of China ; Youth Innovation Promotion Association Chinese Academy of Sciences
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000447819300010
PublisherELSEVIER SCI LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/22801
Collection数字内容技术与服务研究中心_听觉模型与认知计算
Corresponding AuthorYang, Dongbao
Affiliation1.Univ Sci & Technol China, Sch Informat Sci & Technol, Hefei 230026, Anhui, Peoples R China
2.Shandong Univ, Sch Mech Elect & Informat Engn, Weihai 264209, Peoples R China
3.Chinese Acad Sci, Inst Informat Engn, Natl Engn Lab Informat Secur Technol, Beijing 100093, Peoples R China
4.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Xie, Hongtao,Yang, Dongbao,Sun, Nannan,et al. Automated pulmonary nodule detection in CT images using deep convolutional neural networks[J]. PATTERN RECOGNITION,2019,85:109-119.
APA Xie, Hongtao,Yang, Dongbao,Sun, Nannan,Chen, Zhineng,&Zhang, Yongdong.(2019).Automated pulmonary nodule detection in CT images using deep convolutional neural networks.PATTERN RECOGNITION,85,109-119.
MLA Xie, Hongtao,et al."Automated pulmonary nodule detection in CT images using deep convolutional neural networks".PATTERN RECOGNITION 85(2019):109-119.
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