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Computational and Mathematical Methods in Medicine
Volume 2013, Article ID 148363, 11 pages
Research Article

Classification of Pulmonary Nodules by Using Hybrid Features

1Department of Engineering Sciences, Istanbul University, 34320 Avcılar, Istanbul, Turkey
2Department of Electrical and Electronics Engineering, Istanbul University, 34320 Avcılar, Istanbul, Turkey

Received 28 March 2013; Revised 24 May 2013; Accepted 29 May 2013

Academic Editor: Chung-Ming Chen

Copyright © 2013 Ahmet Tartar et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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).