A novel prostate cancer classification technique using intermediate memory tabu search

MA Tahir, A Bouridane, F Kurugollu, A Amira

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)

Abstract

The introduction of multispectral imaging in pathology problems such as the identification of prostatic cancer is recent. Unlike conventional RGB color space, it allows the acquisition of a large number of spectral bands within the visible spectrum. This results in a feature vector of size greater than 100. For such a high dimensionality, pattern recognition techniques suffer from the well-known curse of dimensionality problem. The two well-known techniques to solve this problem are feature extraction and feature selection. In this paper, a novel feature selection technique using tabu search with an intermediate-term memory is proposed. The cost of a feature subset is measured by leave-one-out correct-classification rate of a nearest-neighbor (1-NN) classifier. The experiments have been carried out on the prostate cancer textured multispectral images and the results have been compared with a reported classical feature extraction technique. The results have indicated a significant boost in the performance both in terms of minimizing features and maximizing classification accuracy.
Original languageEnglish
Pages (from-to)2241-2249
JournalEURASIP JOURNAL ON APPLIED SIGNAL PROCESSING
Volume2005
Issue number14
Publication statusPublished (in print/issue) - Aug 2005

Keywords

  • feature selection
  • dimensionality reduction
  • tabu search
  • 1-NN classifier
  • prostate cancer classification

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