Abstract

Lung diseases are the disorders that affect the lungs, the organs that allow us to breathe and it is the most common medical conditions worldwide especially in India. The diseases such as pleural effusion and normal lung are detected and classified in this work. The purpose of the work is to detect and classify the lung diseases by effective feature extraction through moment invariants, feature selection through genetic algorithm and the results are classified by the Naïve bayes and decision tree classifiers. The preprocessing techniques will remove the noises and the feature extraction are done to extract the useful features in the image and the feature selection technique will optimize the top ranking features that are relevant for the image and the classifiers are employed to classify the images and the performance measures are found for the same. The result shows that the Decision tree classifier shows more promising results than the naïve bayes classifier.

Keyword

Classification, Decision tree, Feature Extraction, moment invariants, Preprocessing, Performance.

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