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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Lee, S.L.A. Kouzani, A.Z. Hu, E.J. |
| Copyright Year | 2008 |
| Description | Author affiliation: Sch. of Eng. & IT, Deakin Univ., Waurn Ponds, VIC (Lee, S.L.A.; Kouzani, A.Z.; Hu, E.J.) |
| Abstract | A method is presented for identification of lung nodules. It includes three stages: image acquisition, background removal, and nodule detection. The first stage improves image quality. The second stage extracts long lobe regions. The third stage detects lung nodules. The method is based on the random forest learner. Training set contains nodule, non-nodule, and false-positive patterns. Test set contains randomly selected images. The developed method is compared against the support vector machine. True-positives of 100% and 85.9%, and false-positives of 1.27 and 1.33 per image were achieved by the developed method and the support vector machine, respectively. |
| Sponsorship | IEEE Region 10 |
| Starting Page | 1 |
| Ending Page | 5 |
| File Size | 1245224 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781424424085 |
| DOI | 10.1109/TENCON.2008.4766750 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-11-19 |
| Publisher Place | India |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Lungs Computed tomography Error analysis Cancer Classification tree analysis Voting Support vector machines Training data Australia Image quality |
| Content Type | Text |
| Resource Type | Article |
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