Adaptive simulated annealing for CT image classification
Loomes, Martin J., Albrecht, Andreas A., Steinhoefel, K. and Taupitz, M. (2002) Adaptive simulated annealing for CT image classification. International Journal of Pattern Recognition and Artificial Intelligence, 16 (5) . pp. 573-588. ISSN 0218-0014 [Article]
Abstract
This paper presents a pattern classification method that combines the classical Perceptron algorithm with simulated annealing. The approach is applied to the recognition of focal liver tumors presented in the DICOM format. On test sets of 100+100 examples (disjoint from the learning set) we obtain a correct classification of more than 98%.
This work was carried out as part of a collaboration with medical practitioners based at the Institute of Radiology, Humboldt University of Berlin. This paper builds upon work first presented at ESANN 2001.
| Item Type: | Article |
|---|---|
| Research Areas: | A. > School of Science and Technology A. > School of Science and Technology > Computer Science A. > School of Science and Technology > Computer Science > SensoLab group |
| ISI Impact: | 2 |
| Item ID: | 87 |
| Useful Links: | |
| Depositing User: | Repository team |
| Date Deposited: | 17 Oct 2008 15:40 |
| Last Modified: | 12 Jun 2019 12:41 |
| URI: | https://eprints.mdx.ac.uk/id/eprint/87 |
Actions (login required)
 |
View Item |
Statistics
Additional statistics are available via IRStats2.
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)