Machine learning methods for in vivo skin parameter estimation

Saurabh Vyas, Amit Banerjee, Philippe Burlina

Research output: Contribution to journalArticle

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)
Article number6627860
Pages (from-to)524-525
Number of pages2
JournalProceedings - IEEE Symposium on Computer-Based Medical Systems
StatePublished - Jan 1 2013

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Parameter estimation
Learning systems
Skin
Skin Neoplasms
Neoplasm Metastasis
Imaging techniques
Machine Learning
Neoplasms

ASJC Scopus subject areas

  • Computer Science Applications
  • Radiology Nuclear Medicine and imaging

Cite this

Machine learning methods for in vivo skin parameter estimation. / Vyas, Saurabh; Banerjee, Amit; Burlina, Philippe.

In: Proceedings - IEEE Symposium on Computer-Based Medical Systems, 01.01.2013, p. 524-525.

Research output: Contribution to journalArticle

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