bei Google ScholarTitelangaben
Bocklitz, Thomas ; Putsche, Melanie ; Stüber, Carsten ; Käs, Josef ; Niendorf, Axel ; Rösch, Petra ; Popp, Jürgen:
A comprehensive study of classification methods for medical diagnosis.
In: Journal of Raman Spectroscopy.
Bd. 40
(2009)
Heft 12
.
- S. 1759-1765.
ISSN 1097-4555
DOI: https://doi.org/10.1002/jrs.2529
Abstract
In this model study, we developed a method to distinguish between breast cancer cells and normal epithelial cells, which is in principal suitable for online diagnosis by Raman spectroscopy. Two cell lines were chosen as model systems for cancer and normal tissue. Both cell lines consist of epithelial cells, but the cells of the MCF-7 series are carcinogenic, where the MCF-10A cells are normal growing. An algorithm is presented for distinguishing cells of the MCF-7 and MCF-10A cell lines, which has an accuracy rate of above 99. For this purpose, two classification steps are utilized. The first step, the so-called top-level classifier searches for Raman spectra, which are measured in the nuclei region. In the second step, a wide range of discriminant models are possible and thesemodels are compared. The classification rates are always estimated using a cross-validation and a holdout-validation procedure to ensure the ability of the routine diagnosis to work in clinical environments.
Weitere Angaben
| Publikationsform: | Artikel in einer Zeitschrift |
|---|---|
| Begutachteter Beitrag: | Ja |
| Keywords: | breast cancer; chemometric analysis; pattern recognition; Raman spectroscopy |
| Institutionen der Universität: | Fakultäten > Fakultät für Mathematik, Physik und Informatik > Institut für Informatik > Lehrstuhl Künstliche Intelligenz in der Mikroskopie und Spektroskopie > Lehrstuhl Künstliche Intelligenz in der Mikroskopie und Spektroskopie - Univ.-Prof. Dr. Thomas Wilhelm Bocklitz |
| Titel an der UBT entstanden: | Nein |
| Themengebiete aus DDC: | 500 Naturwissenschaften und Mathematik > 530 Physik |
| Eingestellt am: | 22 Mai 2023 08:58 |
| Letzte Änderung: | 22 Mai 2023 08:58 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/76285 |