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Báo cáo hóa học: "A Novel Prostate Cancer Classification Technique Using Intermediate Memory Tabu Search"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: A Novel Prostate Cancer Classification Technique Using Intermediate Memory Tabu Search | EURASIP Journal on Applied Signal Processing 2005 14 2241-2249 2005 Hindawi Publishing Corporation A Novel Prostate Cancer Classification Technique Using Intermediate Memory Tabu Search Muhammad Atif Tahir School of Computer Science Queen s University of Belfast Belfast BT7 INN Northern Ireland UK Email a.tahir@qub.ac.uk Ahmed Bouridane School of Computer Science Queen s University of Belfast Belfast BT7 inn Northern Ireland UK Email a.bouridane@qub.ac.uk Fatih Kurugollu School of Computer Science Queen s University of Belfast Belfast BT7 INN Northern Ireland UK Email f.kurugollu@qub.ac.uk Abbes Amira School of Computer Science Queen s University of Belfast Belfast BT7 INN Northern Ireland UK Email a.amira@qub.ac.uk Received 3I December 2003 Revised 2 November 2004 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. Keywords and phrases feature selection dimensionality reduction tabu search 1-NN classifier prostate cancer classification. 1. .