Texture Filter Optimization Using Particle Swarm Optimization for Efficient Lung Image Classification

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A. Sathish
S. Ravimaran
M. Nallusamy


Medical Images, Gabor filter, Feature selection, Particle Swarm Optimization (PSO), Naïve Bayes and Random Tree


There are several millions of images that have been generated by the hospitals and healthcare centres on a daily basis. Imaging was employed as the preferred tool of diagnostics by many more medical procedures. Detecting lung cancer at an early stage can to a significant extent enhance survival chances but can also be challenging to detect the various stages of lung cancer with a lesser number of symptoms. Medical image processing is favoured by Gabor filter and for the application of signal processing, it is a sharp cut-off. A step in pre-processing is the next step in machine learning and this is very effective in the reduction of dimensionality and removal of irrelevant data. It also helps in increasing accuracy and in learning accuracy improvement aside from the comprehensibility of results. Particle Swarm Optimization (PSO) has been used widely for solving problems in optimization which is a problem of feature selection. For the PSO, every solution has been looked at as a particle and the algorithm looks for an ideal solution taking into consideration all particles.

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