Pulmonary Nodule Detection Model Based on SVM and CT Image Feature-Level Fusion with Rough Sets
Joint Authors
Zhou, Tao
Lu, Huiling
Zhang, Junjie
Shi, Hongbin
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-09-18
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
In order to improve the detection accuracy of pulmonary nodules in CT image, considering two problems of pulmonary nodules detection model, including unreasonable feature structure and nontightness of feature representation, a pulmonary nodules detection algorithm is proposed based on SVM and CT image feature-level fusion with rough sets.
Firstly, CT images of pulmonary nodule are analyzed, and 42-dimensional feature components are extracted, including six new 3-dimensional features proposed by this paper and others 2-dimensional and 3-dimensional features.
Secondly, these features are reduced for five times with rough set based on feature-level fusion.
Thirdly, a grid optimization model is used to optimize the kernel function of support vector machine (SVM), which is used as a classifier to identify pulmonary nodules.
Finally, lung CT images of 70 patients with pulmonary nodules are collected as the original samples, which are used to verify the effectiveness and stability of the proposed model by four groups’ comparative experiments.
The experimental results show that the effectiveness and stability of the proposed model based on rough set feature-level fusion are improved in some degrees.
American Psychological Association (APA)
Zhou, Tao& Lu, Huiling& Zhang, Junjie& Shi, Hongbin. 2016. Pulmonary Nodule Detection Model Based on SVM and CT Image Feature-Level Fusion with Rough Sets. BioMed Research International،Vol. 2016, no. 2016, pp.1-13.
https://search.emarefa.net/detail/BIM-1098922
Modern Language Association (MLA)
Zhou, Tao…[et al.]. Pulmonary Nodule Detection Model Based on SVM and CT Image Feature-Level Fusion with Rough Sets. BioMed Research International No. 2016 (2016), pp.1-13.
https://search.emarefa.net/detail/BIM-1098922
American Medical Association (AMA)
Zhou, Tao& Lu, Huiling& Zhang, Junjie& Shi, Hongbin. Pulmonary Nodule Detection Model Based on SVM and CT Image Feature-Level Fusion with Rough Sets. BioMed Research International. 2016. Vol. 2016, no. 2016, pp.1-13.
https://search.emarefa.net/detail/BIM-1098922
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1098922