An Automated CAD System for Classification of Lung Module

Abstract

AbstractThe main objective of the proposed work is to develop an automated CAD system for classification of lung nodules using various classifiers from CT images. The classification of nodule and non-nodule patterns in CT is one of the most significant processes during the detection of lung nodule. This helps in detecting the disease at early stage thereby decrease the mortality rate. The developed CAD systems consist of segmentation, feature extraction and classification. For Segmentation we used Fuzzy C Means (FCM) for effective extraction infected region. Later, we extracted features through First order statistics (FOS) and Second order statistics (SOS) and fed into classifiers like DS, RF and BPNN. The experimentation is conducted on LIDC-IDRI dataset and results outperforms well with BPNN when compare to other classifiers.KeywordsSegmentationClassificationCADFeature extraction