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BY 4.0 license Open Access Published by De Gruyter November 26, 2020

Semi-automatic decision-making process in histopathological specimens from Barrett’s carcinoma patients using hyperspectral imaging (HSI)

  • Marianne Maktabi EMAIL logo , Hannes Köhler , Claire Chalopin , Thomas Neumuth , Yannis Wichmann , Boris Jansen-Winkeln , Ines Gockel , Renè Thieme , Henning Ahle , Dietmar Lorenz , Michael Bange and Susanne Braun
From the journal Current Directions in Biomedical Engineering
https://doi.org/10.1515/cdbme-2020-3066
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Abstract

Discrimination of malignant and non-malignant cells of histopathologic specimens is a key step in cancer diagnostics. Hyperspectral Imaging (HSI) allows the acquisition of spectra in the visual and near-infrared range (500-1000nm). HSI can support the identification and classification of cancer cells using machine learning algorithms. In this work, we tested four classification methods on histopathological slides of esophageal adenocarcinoma. The best results were achieved with a Multi-Layer Perceptron. Sensitivity and F1-Score values of 90% were obtained.

Keywords: Hyperspectral Imaging; Barrett’s carcinoma; histopathological slides; machine learning
Published Online: 2020-11-26
Published in Print: 2020-09-01

© 2020 by Walter de Gruyter Berlin/Boston

This work is licensed under the Creative Commons Attribution 4.0 International License.

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From the journal
Current Directions in Biomedical Engineering
Current Directions in Biomedical Engineering
Volume 6 Issue 3

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