Classification of melanoma using wavelet transform-based optimal feature set
Document Type
Conference Proceeding
Publication Date
10-27-2004
Abstract
The features used in the ABCD rule for characterization of skin lesions suggest that the spatial and frequency information in the nevi changes at various stages of melanoma development. To analyze these changes wavelet transform based features have been reported. The classification of melanoma using these features has produced varying results. In this work, all the reported wavelet transform based features are combined to form a single feature set. This feature set is then optimized by removing redundancies using principal component analysis. A feed forward neural network trained with the back propagation algorithm is then used in the classification process to obtain better classification results.
Identifier
5644288612 (Scopus)
Publication Title
Proceedings of SPIE the International Society for Optical Engineering
External Full Text Location
https://doi.org/10.1117/12.536013
ISSN
0277786X
First Page
944
Last Page
951
Volume
5370 II
Recommended Citation
Walvick, Ronn; Patel, Ketan; Patwardhan, Sachin V.; and Dhawan, Atam P., "Classification of melanoma using wavelet transform-based optimal feature set" (2004). Faculty Publications. 20170.
https://digitalcommons.njit.edu/fac_pubs/20170
