NDLI: Feature selection and classification of breast MRI lesions based on multi classifier

Content Provider	IEEE Xplore Digital Library
Author	Keyvanfard, F. Shoorehdeli, M.A. Teshnehlab, M.
Copyright Year	2011
Description	Author affiliation: Electrical and Computer Eng. Dept, KNT University of Technology, Tehran, Iran (Keyvanfard, F.; Teshnehlab, M.) \|\| Electrical and Computer Eng. Dept, KNT University of Technology, Tehran, Iran, Advanced Process Automation and, Control (APAC) (Shoorehdeli, M.A.)
Abstract	This paper aims to increase the classification specificity by using multi classifier system. First, a novel pixel search approach is applied to find significant region in images. Fuzzy C-means is utilized to determine the clear boundary of tumor. Then, shape and texture features are extracted from region of interest. Genetic algorithm is applied to select the best feature used for classifiers. Several neural networks and support vector machine are considered as classifiers that classify the data into benign and malignant group. To improve the performance of classification, three classifiers that have the best results among all applied methods are combined together that they have been named as multi classifier system. For each lesion, final detection as malignant or benign has been evaluated, when the same results are achieved from two classifiers of multi classifier system. Notice that the Jack-Knife technique is applied in this study, because it is useful for small data base as ours gotten from Milad Hospital in Tehran, Iran.
Starting Page	54
Ending Page	58
File Size	608581
Page Count	5
File Format	PDF
ISBN	9781424498338
e-ISBN	9781424498345
DOI	10.1109/AISP.2011.5960979
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-15
Publisher Place	Iran
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Neural network classification Feature selection Multi classifier system Magnetic resonance imaging Artificial neural networks Feature extraction Breast cancer Breast MRI Support vector machine Lesions
Content Type	Text
Resource Type	Article

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