<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Heinz Handels</style></author><author><style face="normal" font="default" size="100%">Ross, T</style></author><author><style face="normal" font="default" size="100%">Kreusch, J</style></author><author><style face="normal" font="default" size="100%">Wolff, H H</style></author><author><style face="normal" font="default" size="100%">Siegfried J. Pöppl</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Computer-Supported Diagnosis of Melanoma in Profilometry</style></title><secondary-title><style face="normal" font="default" size="100%">Methods of information in medicine</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Methods Inf Med</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Diagnosis, Differential</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Image Interpretation, Computer-Assisted</style></keyword><keyword><style  face="normal" font="default" size="100%">Melanoma</style></keyword><keyword><style  face="normal" font="default" size="100%">Neural Networks (Computer)</style></keyword><keyword><style  face="normal" font="default" size="100%">Nevus</style></keyword><keyword><style  face="normal" font="default" size="100%">Skin Neoplasms</style></keyword><keyword><style  face="normal" font="default" size="100%">Surface Properties</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1999</style></year><pub-dates><date><style  face="normal" font="default" size="100%">1999 Mar</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">38</style></volume><pages><style face="normal" font="default" size="100%">43-9</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Laser profilometry offers new possibilities to improve non-invasive tumor diagnostics in dermatology. In this paper, a new approach to computer-supported analysis and interpretation of high-resolution skin-surface profiles of melanomas and nevocellular nevi is presented. Image analysis methods are used to describe the profile's structures by texture parameters based on co-occurrence matrices, features extracted from the Fourier power spectrum, and fractal features. Different feature selection strategies, including genetic algorithms, are applied to determine the best possible subsets of features for the classification task. Several architectures of multilayer perceptrons with error back-propagation as learning paradigm are trained for the automatic recognition of melanomas and nevi. Furthermore, network-pruning algorithms are applied to optimize the network topology. In the study, the best neural classifier showed an error rate of 4.5% and was obtained after network pruning. The smallest error rate in all, of 2.3%, was achieved with nearest neighbor classification.</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/10339963?dopt=Abstract</style></custom1></record></records></xml>