1. Discriminative power of visual attributes in dermatology
- Author
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Nicolai Petkov, Ioannis Giotis, Marcel F. Jonkman, Margaretha Visser, Intelligent Systems, and Translational Immunology Groningen (TRIGR)
- Subjects
Databases, Factual ,skin lesion ,MUTUAL INFORMATION ,Color ,Feature selection ,Dermatology ,Machine learning ,computer.software_genre ,Lexicon ,Information theory ,Models, Biological ,Skin Diseases ,dermatological lexicon ,Discriminative model ,discriminative power ,Added value ,Humans ,Entropy (information theory) ,Diagnosis, Computer-Assisted ,Mathematics ,information theory ,Soundness ,business.industry ,CONSULTATIONS ,Bayes Theorem ,Pattern recognition ,Mutual information ,RELIABILITY ,FEATURE-SELECTION ,Artificial intelligence ,business ,entropy ,computer ,STANDARD DEFINITIONS ,SKIN - 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: The 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.
- Published
- 2013