Document Type
Conference Paper
Publication Date
12-3-2018
Publication Source
Proceedings of the IEEE National Aerospace Electronics Conference, NAECON
Abstract
Lung cancer typically exhibits its presence with the formation of pulmonary nodules. Computer Aided Detection (CAD) of such nodules in CT scans would be of valuable help in lung cancer screening. Typical CAD system is comprised of a candidate detector and a feature-based classifier. In this research, we study and explore the performance of Support Vector Machine (SVM) based on a large set of features. We study the performance of SVM as a function of the number of features. Our results indicate that SVM is more robust and computationally faster with a large set of features and less prone to over-Training when compared to traditional classifiers. In addition, we also present a computationally efficient approach for selecting features for SVM. Results are presented for a publicly available Lung Nodule Analysis 2016 dataset. Our results based on 10-fold validation indicate that SVM based classification method outperforms the fisher linear discriminant classifier by 14.8%.
Inclusive pages
262-266
ISBN/ISSN
0547-3578
Document Version
Postprint
Keywords
Computed Tomography, Computer Aided Detection, Fischer Linear Discriminant Classifier, Lung Nodule, Support Vector Machine, University of Dayton Electro-optics and Photonics
eCommons Citation
Narayanan, Barath; Hardie, Russell C.; and Kebede, Temesguen Messay, "Performance Analysis of Feature Selection Techniques for Support Vector Machine and its Application for Lung Nodule Detection" (2018). Electrical and Computer Engineering Faculty Publications. 435.
https://ecommons.udayton.edu/ece_fac_pub/435
Comments
The document available for download is the authors' accepted manuscript, provided in compliance with the publisher's policy on self-archiving. Permission documentation is on file. To view the version of record, use the DOI: https://doi.org/10.1109/NAECON.2018.8556669