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A novel prostate cancer classification technique using intermediate memory tabu search

Tahir, MA, Bouridane, A, Kurugollu, F and Amira, A (2005) A novel prostate cancer classification technique using intermediate memory tabu search. EURASIP JOURNAL ON APPLIED SIGNAL PROCESSING, 2005 (14). pp. 2241-2249. [Journal article]

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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.

Item Type:Journal article
Keywords:feature selection; dimensionality reduction; tabu search; 1-NN classifier; prostate cancer classification
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Engineering
Research Institutes and Groups:Engineering Research Institute
Engineering Research Institute > Nanotechnology & Integrated BioEngineering Centre (NIBEC)
ID Code:13430
Deposited By: Dr Abbes Amira
Deposited On:20 May 2010 10:33
Last Modified:25 Jul 2011 11:28

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