Classification of Lung Cancer Nodules using SVM Kernels

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 cooccurrence 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.