Computer-Aided Lung Nodule Recognition by SVM Classifier Based on Combination of Random Undersampling and SMOTE
Joint Authors
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-04-06
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
In lung cancer computer-aided detection/diagnosis (CAD) systems, classification of regions of interest (ROI) is often used to detect/diagnose lung nodule accurately.
However, problems of unbalanced datasets often have detrimental effects on the performance of classification.
In this paper, both minority and majority classes are resampled to increase the generalization ability.
We propose a novel SVM classifier combined with random undersampling (RU) and SMOTE for lung nodule recognition.
The combinations of the two resampling methods not only achieve a balanced training samples but also remove noise and duplicate information in the training sample and retain useful information to improve the effective data utilization, hence improving performance of SVM algorithm for pulmonary nodules classification under the unbalanced data.
Eight features including 2D and 3D features are extracted for training and classification.
Experimental results show that for different sizes of training datasets our RU-SMOTE-SVM classifier gets the highest classification accuracy among the four kinds of classifiers, and the average classification accuracy is more than 92.94%.
American Psychological Association (APA)
Sui, Yuan& Wei, Ying& Zhao, Dazhe. 2015. Computer-Aided Lung Nodule Recognition by SVM Classifier Based on Combination of Random Undersampling and SMOTE. Computational and Mathematical Methods in Medicine،Vol. 2015, no. 2015, pp.1-13.
https://search.emarefa.net/detail/BIM-1057877
Modern Language Association (MLA)
Sui, Yuan…[et al.]. Computer-Aided Lung Nodule Recognition by SVM Classifier Based on Combination of Random Undersampling and SMOTE. Computational and Mathematical Methods in Medicine No. 2015 (2015), pp.1-13.
https://search.emarefa.net/detail/BIM-1057877
American Medical Association (AMA)
Sui, Yuan& Wei, Ying& Zhao, Dazhe. Computer-Aided Lung Nodule Recognition by SVM Classifier Based on Combination of Random Undersampling and SMOTE. Computational and Mathematical Methods in Medicine. 2015. Vol. 2015, no. 2015, pp.1-13.
https://search.emarefa.net/detail/BIM-1057877
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1057877