Abstract
Presents a new hybrid system which integrates a neural network and fuzzy rule-based system learning methods. The data used in this study were collected from 100 women who were clinically diagnosed with breast cancer in the form of carcinoma or benign conditions. The data set contains seven different histological and cytological factors, and two nodal outputs (positive and negative nodal status) to be predicted for nodal involvement assessment in breast cancer patients. The hybrid system yielded the highest predictive accuracy of 73%, compared with statistical, neural networks and fuzzy logic methods. The overall results are encouraging and reveal the efficiency of the hybrid system.
| Original language | English |
|---|---|
| Title of host publication | Engineering in Medicine and Biology, 2002. 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society EMBS/BMES Conference, 2002. Proceedings of the Second Joint |
| Publisher | IEEE |
| Pages | 1049-1050 |
| Volume | 2 |
| ISBN (Print) | 0-7803-7612-9 |
| DOIs | |
| Publication status | Published - 2002 |
Bibliographical note
This paper is not available on the repository. The paper was given at the 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society EMBS/BMES Conference, 2002Fingerprint Dive into the research topics of 'A hybrid system for nodal involvement assessment in breast cancer patients'. Together they form a unique fingerprint.
Cite this
Seker, H., Odetayo, M. O., Petrovic, D., & Naguib, R. N. G. (2002). A hybrid system for nodal involvement assessment in breast cancer patients. In Engineering in Medicine and Biology, 2002. 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society EMBS/BMES Conference, 2002. Proceedings of the Second Joint (Vol. 2, pp. 1049-1050). IEEE. https://doi.org/10.1109/IEMBS.2002.1106270