Abstract
This project presents two methods for image classification for the detection of malignant melanoma: The Mahalanobis-Taguchi System and Finite State Classifiers. the Mahalanobis-Taguchi System is a diagnosis and predictive method for analyzing patterns in multivariate cases, while Finite State Classifiers are a state-based machine learning technique. the goal of this study is to compare the ability of the Mahalanobis-Taguchi System and a Finite State Classifier to discriminate using small data sets. We examine the discriminant ability as a function of data set size using publicly available skin lesion image data. While analysis of the data shows a high degree of correlation, the Mahalanobis-Taguchi System performed poorly when trying to discriminate between Malignant Melanoma and benign lesions. Alternately, the Finite State Classifiers developed using evolutionary computation obtained over 85% correct classification of the malignant and benign lesions using the image data sets. © 2011 IEEE.
Recommended Citation
E. A. Cudney and S. M. Corns, "A Comparison of Finite State Classifier and Mahalanobis-Taguchi System for Multivariate Pattern Recognition in Skin Cancer Detection," IEEE SSCI 2011 - Symposium Series on Computational Intelligence - CIBCB 2011: 2011 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, pp. 169 - 175, article no. 5948469, Institute of Electrical and Electronics Engineers, Sep 2011.
The definitive version is available at https://doi.org/10.1109/CIBCB.2011.5948469
Department(s)
Engineering Management and Systems Engineering
International Standard Book Number (ISBN)
978-142449897-0
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
28 Sep 2011
