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Classification of Lung Cancer Nodules using SVM Kernels
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 95 - Number 25 |
| Year of Publication: 2014 |
| Authors: S. Shaik Parveen, C. Kavitha |
10.5120/16751-7013
|
S. Shaik Parveen, C. Kavitha . Classification of Lung Cancer Nodules using SVM Kernels. International Journal of Computer Applications. 95, 25 ( June 2014), 25-28. DOI=10.5120/16751-7013
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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