NDLI: Another Approach to Detection of Abnormalities in MR-Images Using Support Vector Machines

Content Provider	IEEE Xplore Digital Library
Author	Behnamghader, E. Ardekani, R.D. Torabi, M. Fatemizadeh, E.
Copyright Year	2007
Description	Author affiliation: Dept. of Electr. Eng., Sharif Univ. of Technol., Tehran, Iran (Behnamghader, E.; Ardekani, R.D.; Torabi, M.; Fatemizadeh, E.)
Abstract	In this paper we will address two major problems in mammogram analysis for breast cancer in MR-images. The first is classification between normal and abnormal cases and then, discrimination between benign and malignant in cancerous cases. Our proposed method extracts textural and statistical descriptive features that are fed to a learning engine based on the use of support vector machine learning framework to categorize them. The obtained results show excellent accuracy in both classification problems, that proves the appropriate combination of our features and selecting powerful classifier i.e. Support Vector Machine leads us to a brilliant outcome.
Starting Page	98
Ending Page	101
File Size	411597
Page Count	4
File Format	PDF
ISBN	9789531841160
ISSN	18455921
DOI	10.1109/ISPA.2007.4383671
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-09-27
Publisher Place	Turkey
Access Restriction	Subscribed
Rights Holder	Fac. of Elec. Eng. & Computing, Univ of Zagreb, Croatia
Subject Keyword	Support vector machines Mammography Breast cancer Support vector machine classification Feature extraction Artificial neural networks Machine learning Diseases Data analysis Neural networks
Content Type	Text
Resource Type	Article

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