FEATURE SELECTION IN CT IMAGES BASED ON PARTICLE SWARM BASED OPTIMIZATION
Journal Title: Elysium Journal of Engineering Research and Management - Year 2016, Vol 3, Issue 3
Abstract
Lung diseases are the common disorder that can affect the lungs and create breathing problem in all the humans. CT images plays an important role for diagnosing the lung diseases.This project proposes a Particle swarm based optimization technique to select the best features. Initially the features can be extracted based on B-HOG features, Wavelet features, LBP features and CVH features. The feature selection process were employed based on is Particle swarm based optimization (PSO) is the objective function. The selected features were then classified using different classifiers like SVM, Artificial neural network and Fuzzy nearest neighbourhood. For all the considered classifiers, our PSO method brought the better recognition. The advantages on computation effectiveness and efficiency of PSO are shown through experiments. The performance analysis is to calculate the accuracy, sensitivity and specificity.
Authors and Affiliations
Cecil Theijas M. , Varatharajan R.
Keywords
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