Lung nodules detection by ensemble classification

A. Z. Kouzani, S. L. A. Lee, E. J. Hu
2008 Conference Proceedings / IEEE International Conference on Systems, Man and Cybernetics  
A method is presented that achieves lung nodule detection by classification of nodule and non-nodule patterns. It is based on random forests which are ensemble learners that grow classification trees. Each tree produces a classification decision, and an integrated output is calculated. The performance of the developed method is compared against that of the support vector machine and the decision tree methods. Three experiments are performed using lung scans of 32 patients including thousands of
more » ... images within which nodule locations are marked by expert radiologists. The classification errors and execution times are presented and discussed. The lowest classification error (2.4%) has been produced by the developed method.
doi:10.1109/icsmc.2008.4811296 dblp:conf/smc/KouzaniLH08 fatcat:h7xdklyvwveyfnvl3lxm3r7dmq