EAN: 9783961474455

MACHINE LEARNING AND HYPER SPECTRAL IMAGING MUL...

Worldwide  carcinomas are the second leading cause of death after cardiovascular diseases. Of
these  more than 25 % are caused by cancer in the gastrointestinal tract. However  screening
still misses about 20 % of carcinomas. An optimal solution would be a red flag technology to
locate the suspicious area. Despite the use of many optical technologies and advancements in
traditional endoscopic technologies  most methods have failed to significantly improve the
detectability of carcinomas in the GI. Therefore  it has been suggested in the literature to
use spectroscopic quantitative measurements  which is achieved by hyperspectral imaging.
However  in this work  a multispectral endoscope system for in vivo use is used for human
(stomach) and mouse (colon) studies. In the human study  the tumours could only be found to a
limited extent with the standard methods (MCC = 0.32  ACC2=0.68). Nevertheless  this is a
significant improvement compared to previous results. The introduction of spectral spatial
variation as a spatial-spectral feature improves the results significantly again. Compared to
the human study  the results from the mouse model show better classification results: ACC2 =
0.73 and MCC = 0.47. Thus  for the development of an endoscopic red flag technique  a point
technique for correct pre-labelling of the data is needed. Moreover  the results of this MSI in
vivo study with spatial features are similar to the recent HSI ex vivo studies. Therefore  it
is likely that MSI will remain part of future research.

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