- Research Article
- Open Access
A Novel Prostate Cancer Classification Technique Using Intermediate Memory Tabu Search
EURASIP Journal on Advances in Signal Processing volume 2005, Article number: 906054 (2005)
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.
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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Tahir, M.A., Bouridane, A., Kurugollu, F. et al. A Novel Prostate Cancer Classification Technique Using Intermediate Memory Tabu Search. EURASIP J. Adv. Signal Process. 2005, 906054 (2005). https://doi.org/10.1155/ASP.2005.2241
Keywords and phrases
- feature selection
- dimensionality reduction
- tabu search
- 1-NN classifier
- prostate cancer classification