Classification of melanoma using wavelet transform-based optimal feature set

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

Research output: Contribution to journalConference articlepeer-review

11 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)944-951
Number of pages8
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume5370 II
DOIs
StatePublished - Oct 27 2004
EventProgress in Biomedical Optics and Imaging - Medical Imaging 2004: Imaging Processing - San Diego, CA, United States
Duration: Feb 16 2004Feb 19 2004

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Keywords

  • Melanoma
  • Neural Networks
  • Principal Component Analysis
  • Wavelet Transform

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