Classification of Pulmonary Nodules by Using Hybrid Features
Joint Authors
Tartar, Ahmet
Akan, Aydin
Kilic, Niyazi
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-06-25
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Early detection of pulmonary nodules is extremely important for the diagnosis and treatment of lung cancer.
In this study, a new classification approach for pulmonary nodules from CT imagery is presented by using hybrid features.
Four different methods are introduced for the proposed system.
The overall detection performance is evaluated using various classifiers.
The results are compared to similar techniques in the literature by using standard measures.
The proposed approach with the hybrid features results in 90.7% classification accuracy (89.6% sensitivity and 87.5% specificity).
American Psychological Association (APA)
Tartar, Ahmet& Kilic, Niyazi& Akan, Aydin. 2013. Classification of Pulmonary Nodules by Using Hybrid Features. Computational and Mathematical Methods in Medicine،Vol. 2013, no. 2013, pp.1-11.
https://search.emarefa.net/detail/BIM-449602
Modern Language Association (MLA)
Tartar, Ahmet…[et al.]. Classification of Pulmonary Nodules by Using Hybrid Features. Computational and Mathematical Methods in Medicine No. 2013 (2013), pp.1-11.
https://search.emarefa.net/detail/BIM-449602
American Medical Association (AMA)
Tartar, Ahmet& Kilic, Niyazi& Akan, Aydin. Classification of Pulmonary Nodules by Using Hybrid Features. Computational and Mathematical Methods in Medicine. 2013. Vol. 2013, no. 2013, pp.1-11.
https://search.emarefa.net/detail/BIM-449602
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-449602
