A Hybrid Approach for Automated Detection of Lung Nodules in CT Images

Dehmeshki, Jamshid; Ye, X.; Casique, M.V.; Lin, Xinyu

doi:10.1109/isbi.2006.1624964

Cited by 5 publications

(1 citation statement)

References 10 publications

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“…Dehmeshki et al [7] developed a shape based genetic algorithm-based template matching method for detection of lung nodules. 3D geometric shape feature was calculated at each voxel and then combined into global nodule intensity distribution.…”

Section: Introductionmentioning

confidence: 99%

Lung nodules detection by ensemble classification

Kouzani

Lee

2008

2008 IEEE International Conference on Systems, Man and Cybernetics

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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 of 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.

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Section: Introductionmentioning

confidence: 99%