Detection of Lung Disease using an Efficient Image Classification Technique

Image classification is an area of research and exploration. Many researchers and scientists have been trying to find better and more efficient methods for various respective fields. But still, a lot of work is being done and yet to be done in this vast area. We have proposed a technique that uses the two points, i.e., feature subset selection and classification. An embedded model is proposed having a feature subset selection strategy and classification strategy. A lung cancer data set is attained from the UCI repository for analyzing the results. The first size of the initial feature set is reduced using feature selection techniques and then a classification strategy is applied to the attained reduced feature set to achieve good performance.