An Image based Diagnostic System for Lung Disease Classification

Bhuvaneswari Chandran

Abstract


Model-based detection and classification of nodules are two major steps in CAD systems design and evaluation. A common health problem, lung diseases are the most prevailing medical conditions throughout the world. In this paper, Lung diseases are automatically classified as Emphysema, Bronchitis, Pleural effusion and normal lung.The lung CT images are taken as input, preprocessing is applied, feature extraction is done by various methods such as Gabor filter extracts the texture features, walsh hadamard transform extracts the pixel co-efficient values, and a fusion method is proposed in this work which extracts the median absolute deviation values. Feature selection including statistical correlation based methods and Genetic Algorithm for searching in feature vector space are investigated. Four types of the classifiers are used where the Multi-Layer Perceptron Neural Network (MLP-NN) classifier with proposed fusion feature extraction method, genetic algorithm feature selection method gives promising result of 91% accuracy than J48, K- Nearest Neighbour and Naïve bayes classifiers.


Keywords


classification; classifiers; detection; feature extraction; feature selection; fusion; Genetic Algorithm; lung nodules; K- Nearest Neighbour; textures;

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References


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DOI: http://dx.doi.org/10.22385/jctecs.v3i0.6