A Novel Prostate Cancer Classification Technique Using Intermediate Memory Tabu Search
<p/> <p>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 vect...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2005-01-01
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| Series: | EURASIP Journal on Advances in Signal Processing |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1155/ASP.2005.2241 |
| Summary: | <p/> <p>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 <it>tabu search</it> 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.</p> |
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| ISSN: | 1687-6172 1687-6180 |