A new method for pulmonary nodule detection using decision trees

A computer-aided detection (CAD) can help radiologists in diagnosing of lung diseases at an early level. In this study, a new CAD system for pulmonary nodule detection from CT imagery is presented by using morphological features and patient information properties. Decision trees are utilized for cla...

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Bibliographic Details
Main Authors: Tartar, A., Kilic, N., Akan, A.
Format: Conference Proceeding
Language:English
Subjects:
Biomedical imaging
Computed tomography
Design automation
Feature extraction
Radio frequency
Sensitivity
Vegetation
Online Access:Request full text
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Summary:A computer-aided detection (CAD) can help radiologists in diagnosing of lung diseases at an early level. In this study, a new CAD system for pulmonary nodule detection from CT imagery is presented by using morphological features and patient information properties. Decision trees are utilized for classification and overall detection performance is evaluated. Results are compared to similar techniques in the literature by using standard measures. Proposed CAD system with random forest classifier result in 90.5 % sensitivity and 87.6 % specificity of detection performance.
ISSN:1094-687X
1558-4615
2694-0604
DOI:10.1109/EMBC.2013.6611257