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Skin lesion feature vectors classification in models of a Riemannian manifold

Skin lesion feature vectors classification in models of a Riemannian manifold This study is a continuation of a work published by Mete, Ou, and Sirakov (2012), where a model of a 4D manifold of feature vectors was developed. The present paper introduces an improved metric in the 4D manifold first and then extends both the size of the sample space and the dimension (to 6D) of the manifold model in which the sample space lies. As a result, we not only overcame the issue of one single vector representing multiple skin lesions, which occurred in the work of Mete, Ou, and Sirakov (2012), but also improved the accuracy of classification. Furthermore, a statistical evaluation of our support vector machine (SVM) classification method was performed. The intervals of confidence were calculated for the mean of classification of a large sample set in the 6D model. Comparison results of classification with our SVM in 4D and 6D models using 10-fold cross-validation are given at the end of the paper. It is found that the 6D model improves the classification results of the previous study suggesting that two newly introduced features contributed to the increase of the classification accuracy. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annals of Mathematics and Artificial Intelligence Springer Journals

Skin lesion feature vectors classification in models of a Riemannian manifold

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References (31)

Publisher
Springer Journals
Copyright
Copyright © 2014 by Springer International Publishing Switzerland
Subject
Computer Science; Artificial Intelligence (incl. Robotics); Mathematics, general; Computer Science, general; Statistical Physics, Dynamical Systems and Complexity
ISSN
1012-2443
eISSN
1573-7470
DOI
10.1007/s10472-014-9424-8
Publisher site
See Article on Publisher Site

Abstract

This study is a continuation of a work published by Mete, Ou, and Sirakov (2012), where a model of a 4D manifold of feature vectors was developed. The present paper introduces an improved metric in the 4D manifold first and then extends both the size of the sample space and the dimension (to 6D) of the manifold model in which the sample space lies. As a result, we not only overcame the issue of one single vector representing multiple skin lesions, which occurred in the work of Mete, Ou, and Sirakov (2012), but also improved the accuracy of classification. Furthermore, a statistical evaluation of our support vector machine (SVM) classification method was performed. The intervals of confidence were calculated for the mean of classification of a large sample set in the 6D model. Comparison results of classification with our SVM in 4D and 6D models using 10-fold cross-validation are given at the end of the paper. It is found that the 6D model improves the classification results of the previous study suggesting that two newly introduced features contributed to the increase of the classification accuracy.

Journal

Annals of Mathematics and Artificial Intelligence – Springer Journals

Published: Jun 15, 2014

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