Dermatology 2013

Notes: Gilmore, Stephen J

eng

Review

Australia

2012/04/18 06:00

Australas J Dermatol. 2013 Feb;54(1):1-8. doi: 10.1111/j.1440-0960.2012.00883.x. Epub 2012 Apr 16.

URL: http://www.ncbi.nlm.nih.gov/pubmed/22506776

Author Address: Dermatology Research Centre, University of Queensland, School of Medicine, Princess Alexandra Hospital, Brisbane, Australia. s.gilmore1@uq.edu.au

 

 

Reference Type:  Journal Article

Record Number: 4580Author: Giotis, I., Visser, M., Jonkman, M. and Petkov, N.

Year: 2013

Title: Discriminative power of visual attributes in dermatology

Journal: Skin Res Technol

Volume: 19

Issue: 1

Pages: e123-31

Date: Feb

Short Title: Discriminative power of visual attributes in dermatology

Alternate Journal: Skin research and technology : official journal of International Society for Bioengineering and the Skin

ISSN: 1600-0846 (Electronic)

0909-752X (Linking)

DOI: 10.1111/j.1600-0846.2012.00618.x

Accession Number: 22724513

Keywords: Bayes Theorem

Color

Databases, Factual

Dermatology/*methods/standards

Diagnosis, Computer-Assisted/*methods/standards

Entropy

Humans

*Information Theory

*Models, Biological

Skin/pathology

Skin Diseases/classification/*pathology

Abstract: BACKGROUND/PURPOSE: Visual characteristics such as color and shape of skin lesions play an important role in the diagnostic process. In this contribution, we quantify the discriminative power of such attributes using an information theoretical approach. METHODS: We estimate the probability of occurrence of each attribute as a function of the skin diseases. We use the distribution of this probability across the studied diseases and its entropy to define the discriminative power of the attribute. The discriminative power has a maximum value for attributes that occur (or do not occur) for only one disease and a minimum value for those which are equally likely to be observed among all diseases. RESULTS: Verrucous surface, red and brown colors, and the presence of more than 10 lesions are among the most informative attributes. A ranking of attributes is also carried out and used together with a naive Bayesian classifier, yielding results that confirm the soundness of the proposed method. CONCLUSION: proposed measure is proven to be a reliable way of assessing the discriminative power of dermatological attributes, and it also helps generate a condensed dermatological lexicon. Therefore, it can be of added value to the manual or computer-aided diagnostic process.

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