Machine learning methods for in vivo skin parameter estimation

Saurabh Vyas, Amit Banerjee, Philippe Burlina

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The WHO estimates three million new cases of skin cancer each year. Therefore, there exists a need for prescreening tools that can estimate the biological parameters of human skin, as they can help detect cancers before metastasis. In this paper, we present a novel inverse modeling technique based on Kubelka-Munk theory and machine learning to estimate biological skin parameters from in vivo hyperspec-tral imaging. We use the k-nearest neighbors (k-NN) algorithm in order to estimate skin parameters from their hy-perspectral signatures. We test our methods on 241 hyper-spectral signatures obtained from both genders and three ethnicities, and find encouraging results.

Original languageEnglish (US)
Title of host publicationProceedings of CBMS 2013 - 26th IEEE International Symposium on Computer-Based Medical Systems
Pages524-525
Number of pages2
DOIs
StatePublished - Dec 9 2013
Event26th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2013 - Porto, Portugal
Duration: Jun 20 2013Jun 22 2013

Publication series

NameProceedings of CBMS 2013 - 26th IEEE International Symposium on Computer-Based Medical Systems

Other

Other26th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2013
CountryPortugal
CityPorto
Period6/20/136/22/13

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics

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    Vyas, S., Banerjee, A., & Burlina, P. (2013). Machine learning methods for in vivo skin parameter estimation. In Proceedings of CBMS 2013 - 26th IEEE International Symposium on Computer-Based Medical Systems (pp. 524-525). [6627860] (Proceedings of CBMS 2013 - 26th IEEE International Symposium on Computer-Based Medical Systems). https://doi.org/10.1109/CBMS.2013.6627860