NDLI: Multi-scoring feature selection method based on SVM-RFE for prostate cancer diagnosis

Content Provider	IEEE Xplore Digital Library
Author	Albashish, D. Sahran, S. Abdullah, A. Adam, A. Abd Shukor, N. Pauzi, S.H.M.
Copyright Year	2015
Description	Author affiliation: Center for Artificial Intell. Technol., Univ. Kebangsaan Malaysia, Bangi, Malaysia (Albashish, D.; Sahran, S.; Abdullah, A.; Adam, A.) \|\| Dept. of Pathology, Univ. Kebangsaan Malaysia Med. Center, Malaysia (Abd Shukor, N.; Pauzi, S.H.M.)
Abstract	Prostate cancer diagnosis is based mainly by microscopic evaluation of prostate tissue biopsy which includes assigning cancer grading. The latter is crucial in evaluating the prognosis or cancer progression and treatment. The common grading system used is Gleason grading system that classifies the prostate cancer into five basic grades based on the architecture and pattern of glandular proliferation. However, this process may be subjected to inter and intra observer variation. Therefore, the main aim of this paper is to develop a computer aided diagnosis (CAD) utilizing supervised machine learning techniques for Gleason grading of prostate histology. The proposed procedure utilizes the main tissue components of the images in an ensemble style to correctly classify the input histopathological image into benign or malignant. Moreover, the texture features of the benign and malignant images can be used to build the proposed ensemble framework. However, not all extracted texture features contribute to the improvement of the classification performance of the proposed ensemble framework. Therefore, to select the more informative features from a set is a critical issue. In this study, a new multi-scoring features selection method based on SVM-RFE and conditional mutual information (CMI) is proposed.
Starting Page	682
Ending Page	686
File Size	896270
Page Count	5
File Format	PDF
ISBN	9781467367783
ISSN	21556830
e-ISBN	9781467373197
DOI	10.1109/ICEEI.2015.7352585
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-10
Publisher Place	Indonesia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Feature selection Image color analysis Redundancy SVM-RFE Tissue components Feature extraction Ensemble classification SVM Prostate cancer Mutual information CMI
Content Type	Text
Resource Type	Article

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