Classification of melanoma using wavelet transform-based optimal feature set

Ronn P. Walvick; Ketan Patel; Sachin V. Patwardhan; Atam P. Dhawan

doi:10.1117/12.536013

12 May 2004 Classification of melanoma using wavelet-transform-based optimal feature set

Ronn P. Walvick, Ketan Patel, Sachin V. Patwardhan, Atam P. Dhawan

Author Affiliations +

Proceedings Volume 5370, Medical Imaging 2004: Image Processing; (2004) https://doi.org/10.1117/12.536013
Event: Medical Imaging 2004, 2004, San Diego, California, United States

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.

Citation Download Citation

Ronn P. Walvick, Ketan Patel, Sachin V. Patwardhan, and Atam P. Dhawan "Classification of melanoma using wavelet-transform-based optimal feature set", Proc. SPIE 5370, Medical Imaging 2004: Image Processing, (12 May 2004); https://doi.org/10.1117/12.536013

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