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: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2005-01-01
|
| Series: | EURASIP Journal on Advances in Signal Processing |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1155/ASP.2005.2241 |
| _version_ | 1831724474535247872 |
|---|---|
| author | Tahir Muhammad Atif Bouridane Ahmed Kurugollu Fatih Amira Abbes |
| author_facet | Tahir Muhammad Atif Bouridane Ahmed Kurugollu Fatih Amira Abbes |
| author_sort | Tahir Muhammad Atif |
| collection | DOAJ |
| description | <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> |
| first_indexed | 2024-12-21T04:22:27Z |
| format | Article |
| id | doaj.art-bc6967606fd548dbbe77505ce0e16b23 |
| institution | Directory Open Access Journal |
| issn | 1687-6172 1687-6180 |
| language | English |
| last_indexed | 2024-12-21T04:22:27Z |
| publishDate | 2005-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Advances in Signal Processing |
| spelling | doaj.art-bc6967606fd548dbbe77505ce0e16b232022-12-21T19:16:09ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61721687-61802005-01-01200514906054A Novel Prostate Cancer Classification Technique Using Intermediate Memory Tabu SearchTahir Muhammad AtifBouridane AhmedKurugollu FatihAmira Abbes<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>http://dx.doi.org/10.1155/ASP.2005.2241feature selectiondimensionality reductiontabu search1-NN classifierprostate cancer classification |
| spellingShingle | Tahir Muhammad Atif Bouridane Ahmed Kurugollu Fatih Amira Abbes A Novel Prostate Cancer Classification Technique Using Intermediate Memory Tabu Search EURASIP Journal on Advances in Signal Processing feature selection dimensionality reduction tabu search 1-NN classifier prostate cancer classification |
| title | A Novel Prostate Cancer Classification Technique Using Intermediate Memory Tabu Search |
| title_full | A Novel Prostate Cancer Classification Technique Using Intermediate Memory Tabu Search |
| title_fullStr | A Novel Prostate Cancer Classification Technique Using Intermediate Memory Tabu Search |
| title_full_unstemmed | A Novel Prostate Cancer Classification Technique Using Intermediate Memory Tabu Search |
| title_short | A Novel Prostate Cancer Classification Technique Using Intermediate Memory Tabu Search |
| title_sort | novel prostate cancer classification technique using intermediate memory tabu search |
| topic | feature selection dimensionality reduction tabu search 1-NN classifier prostate cancer classification |
| url | http://dx.doi.org/10.1155/ASP.2005.2241 |
| work_keys_str_mv | AT tahirmuhammadatif anovelprostatecancerclassificationtechniqueusingintermediatememorytabusearch AT bouridaneahmed anovelprostatecancerclassificationtechniqueusingintermediatememorytabusearch AT kurugollufatih anovelprostatecancerclassificationtechniqueusingintermediatememorytabusearch AT amiraabbes anovelprostatecancerclassificationtechniqueusingintermediatememorytabusearch AT tahirmuhammadatif novelprostatecancerclassificationtechniqueusingintermediatememorytabusearch AT bouridaneahmed novelprostatecancerclassificationtechniqueusingintermediatememorytabusearch AT kurugollufatih novelprostatecancerclassificationtechniqueusingintermediatememorytabusearch AT amiraabbes novelprostatecancerclassificationtechniqueusingintermediatememorytabusearch |