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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 ofdoi:10.1109/icsmc.2008.4811296 dblp:conf/smc/KouzaniLH08 fatcat:h7xdklyvwveyfnvl3lxm3r7dmq