Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Floating search methods in feature selection
Pattern Recognition Letters
The nature of statistical learning theory
The nature of statistical learning theory
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
An introduction to variable and feature selection
The Journal of Machine Learning Research
Linear Dimensionality Reduction via a Heteroscedastic Extension of LDA: The Chernoff Criterion
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Comparative Study of Texture Features for the Discrimination of Gastric Polyps in Endoscopic Video
CBMS '05 Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems
Pit Pattern Classification of Zoom-Endoscopical Colon Images Using DCT and FFT
CBMS '07 Proceedings of the Twentieth IEEE International Symposium on Computer-Based Medical Systems
Color and Position versus Texture Features for Endoscopic Polyp Detection
BMEI '08 Proceedings of the 2008 International Conference on BioMedical Engineering and Informatics - Volume 02
Automated classification of duodenal imagery in celiac disease using evolved Fourier feature vectors
Computer Methods and Programs in Biomedicine
Pattern Analysis & Applications
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
IEEE Transactions on Information Technology in Biomedicine
Computer-aided tumor detection in endoscopic video using color wavelet features
IEEE Transactions on Information Technology in Biomedicine
Computer Methods and Programs in Biomedicine
| Hi-index | 0.01 |
This paper describes an application of machine learning techniques and evolutionary algorithms to colon cancer diagnosis. We propose an automated classification system for endoscopical images, which is supposed to support physicians in making correct decisions. Classification is done according to the pit-pattern scheme, which defines two/six different classes based on the occurrence of patterns on the mucosa. All discriminative information for classification is obtained by filtering an image's frequency domain. A major part of this paper is devoted to the search for proper frequency filters. An extensive experimental study compares different search strategies and the resulting classification accuracies. We result in a top classification accuracy of 96.9% and 86.8% for the two-and six-classes case, respectively, using a database of 484 zoom-endoscopic images. We observe a tendency toward the employment of lower frequency filter structures for the best classification settings.