Literature by the same author
plus at Google Scholar

Bibliografische Daten exportieren
 

A comprehensive study of classification methods for medical diagnosis

Title data

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. Vol. 40 (2009) Issue 12 . - pp. 1759-1765.
ISSN 1097-4555
DOI: https://doi.org/10.1002/jrs.2529

Abstract in another language

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.

Further data

Item Type: Article in a journal
Refereed: Yes
Keywords: breast cancer; chemometric analysis; pattern recognition; Raman spectroscopy
Institutions of the University: Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Lehrstuhl Künstliche Intelligenz in der Mikroskopie und Spektroskopie > Lehrstuhl Künstliche Intelligenz in der Mikroskopie und Spektroskopie - Univ.-Prof. Dr. Thomas Wilhelm Bocklitz
Result of work at the UBT: No
DDC Subjects: 500 Science > 530 Physics
Date Deposited: 22 May 2023 08:58
Last Modified: 22 May 2023 08:58
URI: https://eref.uni-bayreuth.de/id/eprint/76285