Comparative investigation of different feature extraction techniques for lung cancer detection system

Abstract

The present work demonstrates the utilization of computer-aided diagnosis system for the detection of lung cancer diseases using computer tomography (CT) images, magnetic resonance images (MRI) and X-ray images. The feature extraction process in lung cancer images has been achieved by scale invariant feature transform (SIFT), speeded up robust features (SURF), and principal component analysis (PCA) techniques. In this work, a comparative investigation of different feature extraction technique such as SIFT, SURF, and PCA has been discussed in order to find the best descriptor for feature extraction of cancerous subjects to the normal subjects in terms of two parameters named as execution time and error rate. The main aspect of these learning approaches is to find the valid key points in minimum execution time with least error. The results reveal that the SURF technique has an average execution time of 0.448 s with an average error rate value of 25.704 which is least among three techniques. Hence, SURF extraction technique is best as compared to SIFT and PCA.