-
Conf Proc IEEE Eng Med Biol Soc · Jan 2010
Symbolic learning supporting early diagnosis of melanoma.
- Grzegorz Surówka.
- Department of Physics, Astronomy, and Applied Computer Science, Jagiellonian University, Poland. grzegorz.surowka@uj.edu.pl
- Conf Proc IEEE Eng Med Biol Soc. 2010 Jan 1; 2010: 4104-7.
AbstractWe present a classification analysis of the pigmented skin lesion images taken in white light based on the inductive learning methods by Michalski (AQ). Those methods are developed for a computer system supporting the decision making process for early diagnosis of melanoma. Symbolic (machine) learning methods used in our study are tested on two types of features extracted from pigmented lesion images: coloristic/geometric features, and wavelet-based features. Classification performance with the wavelet features, although achieved with simple rules, is very high. Symbolic learning applied to our skin lesion data seems to outperform other classical machine learning methods, and is more comprehensive both in understanding, and in application of further improvements.
Notes
Knowledge, pearl, summary or comment to share?You can also include formatting, links, images and footnotes in your notes
- Simple formatting can be added to notes, such as
*italics*,_underline_or**bold**. - Superscript can be denoted by
<sup>text</sup>and subscript<sub>text</sub>. - Numbered or bulleted lists can be created using either numbered lines
1. 2. 3., hyphens-or asterisks*. - Links can be included with:
[my link to pubmed](http://pubmed.com) - Images can be included with:
 - For footnotes use
[^1](This is a footnote.)inline. - Or use an inline reference
[^1]to refer to a longer footnote elseweher in the document[^1]: This is a long footnote..