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Classification of Lung Diseases Using Optimization Techniques


Tejinder Kaur , Amritsar College Of Engineering and Technology; Er. Neelakshi Gupta, Amritsar College of Engineering and Technology


Classification, Genetic Algorithm, CT, Particle Swarm Optimization, Feature Extraction


The lungs are part of complex apparatus, expanding and relaxing thousands of times each day to bring in oxygen and expel carbon dioxide. Lung disease can results from problems in any part of this system. In this paper, an automated approach for feature selection and classification of the lung diseases using CT images is presented. The lung CT image is engaged as the input. Filters are used to remove unwanted noise and enhance the quality of image. New feature selection technique that is hybridization of genetic and PSO is used to select features after extracting features using MAD technique. Once features have been selected then they are classified using MLP-NN classifier. In this paper optimization techniques such as genetic/Particle Swarm Optimization are used for feature selection process.

Other Details

Paper ID: IJSRDV3I80412
Published in: Volume : 3, Issue : 8
Publication Date: 01/11/2015
Page(s): 852-854

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