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Classification of Lung Cancer Nodules using SVM Kernels

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International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 95 - Number 25
Year of Publication: 2014
Authors:
S. Shaik Parveen
C. Kavitha
10.5120/16751-7013

Shaik S Parveen and C Kavitha. Article: Classification of Lung Cancer Nodules using SVM Kernels. International Journal of Computer Applications 95(25):25-28, June 2014. Full text available. BibTeX

@article{key:article,
	author = {S. Shaik Parveen and C. Kavitha},
	title = {Article: Classification of Lung Cancer Nodules using SVM Kernels},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {95},
	number = {25},
	pages = {25-28},
	month = {June},
	note = {Full text available}
}

Abstract

Support Vector Machines (SVM) is a machine learning method used for classifying the system. It analyses and identifies the categories using the trained data. It is widely used in medical field for diagnosing the disease. The proposed method consists of four phases. They are lung extraction, segmentation of lung region, feature extraction and finally classification of normal, benign and malignancy in the lung. Threat pixel identification with region growing method is used for segmentation of focal areas in the lung. For feature extraction gray level co- occurrence Matrix (GLCM) is been used. Extracted features are classified using different kernels of Support Vector Machine (SVM). The experimentation is performed with the help of real time computer tomography images.

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