A Comparison of wavelet and curvelet for lung cancer diagnosis with a new Cluster K-Nearest Neighbor classifier

This paper presents a comparison of wavelet and curvelet for lung cancer in term of diagnostic accuracy when each one is applied separately to the cluster K-Nearest neighbor classifier. Lung cancer is among the diseases that lead to high mortality rate globally. The computer aided diagnoisis system that is shown in this paper consists of a preprocessing state, a feature extraction stage (wavelet or curvelet), a feature selection stage and finally a classification stage. The results obtained on the x-ray dataset that was utilized suggest that wavelet produce better accuracy with low false positives and false negatives compared to curvelet. Key-Words: lung cancer; curvelet; wavelet; computer aided diagnosis; feature selection; Cluster-k-NN