Feature Selection and Performance Evaluation of Support Vector Machine (SVM)-Based Classifier for Differentiating Benign and Malignant Pulmonary Nodules by Computed Tomography-Reference-Cited by-同舟云学术

Feature Selection and Performance Evaluation of Support Vector Machine (SVM)-Based Classifier for Differentiating Benign and Malignant Pulmonary Nodules by Computed Tomography

Published:2009-02-26 Issue:1 Volume:23 Page:51-65
ISSN:0897-1889
Container-title:Journal of Digital Imaging
language:en
Short-container-title:J Digit Imaging

Author:

Zhu Yanjie,Tan Yongqiang,Hua Yanqing,Wang Mingpeng,Zhang Guozhen,Zhang Jianguo

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Radiology Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Link

http://link.springer.com/content/pdf/10.1007/s10278-009-9185-9.pdf

Reference26 articles.

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2. McNitt-Gray MF, Hart EM, Goldin JG, Yao CW, Aberle DR: A pattern classification approach to characterizing solitary pulmonary nodules imaged on high resolution computed tomography. Proc SPIE 2710:1024–1034, 1996

3. Nakamura K, Yoshida H, Engelmann R, MacMahon H: Computerized analysis of the likelihood of malignancy in solitary pulmonary nodules with use of artificial neural networks. Radiology 214:823–830, 2000

4. Shiraishi J, Abe H, Englemann R, Aoyama M: Computer-aided diagnosis to distinguish benign from malignant solitary pulmonary nodules on radiographs: ROC analysis of radiologists′ performance–initial experience. Radiology 227:469–474, 2003

5. Kawata Y, Niki N, Ohmatsu H, Kusumoto M, et al: Hybrid classification approach of malignant and benign pulmonary nodules based on topological and histogram features. In: Proc MICCAI 297–306, 2000

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