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Statistical Feature-based Neural Network Approach for the Detection of Lung Cancer
K.A.G. Udeshani, R.G.N. Meegama, T.G.I. Fernando
Pages - 425 - 434     |    Revised - 01-09-2011     |    Published - 05-10-2011
Volume - 5   Issue - 4    |    Publication Date - September / October 2011  Table of Contents
Lung Nodule, Computer Assisted Diagnostic, Chest Radiography
Lung cancer, if successfully detected at early stages, enables many treatment options, reduced risk of invasive surgery and increased survival rate. This paper presents a novel approach to detect lung cancer from raw chest X-ray images. At the first stage, we use a pipeline of image processing routines to remove noise and segment the lung from other anatomical structures in the chest X-ray and extract regions that exhibit shape characteristics of lung nodules. Subsequently, first and second order statistical texture features are considered as the inputs to train a neural network to verify whether a region extracted in the first stage is a nodule or not . The proposed approach detected nodules in the diseased area of the lung with an accuracy of 96% using the pixel-based technique while the feature-based technique produced an accuracy of 88%.
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Dr. K.A.G. Udeshani
Excel Technology Lanka Ltd - Sri Lanka
Dr. R.G.N. Meegama
University of Sri Jayewardenepura - Sri Lanka
Dr. T.G.I. Fernando
University of Sri Jayewardenepura - Sri Lanka

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