NDLI: Wavelet based features of circular scan lines for mammographic mass classification

Content Provider	IEEE Xplore Digital Library
Author	Dash, J.K. Sahoo, L.
Copyright Year	2012
Description	Author affiliation: Department of E&ECE, Indian Institute of Technology, Kharagpur, India (Dash, J.K.; Sahoo, L.)
Abstract	Breast cancer is reported as the second most deadly cancer in the world on which public awareness has been increasing during the last few decades. Early detection can play an effective role in prevention and the most reliable detection technology is mammography. At the early stages of breast cancer, the clinical signs are very mild and vary in appearance, making diagnosis difficult even for specialists. Therefore, automatic reading of medical images becomes highly desirable. This paper aims to develop an automated system for mass classification in digital mammograms. Mini - MIAS database is used to obtain mammogram images. A novel approach for feature extraction is proposed which exploits the wavelet features of radial and circular scan lines drawn over the region of interest (ROI). The discriminating ability of these features are evaluated using three classifiers such as Neural Network (Scaled conjugate back propagation), Bayesian and Support Vector Machine (SVM). The experimental results show that SVM outperforms with an accuracy of 85.96%.
Starting Page	58
Ending Page	61
File Size	324228
Page Count	4
File Format	PDF
ISBN	9781457706943
e-ISBN	9781457706974
DOI	10.1109/RAIT.2012.6194480
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-03-15
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wavelet transforms Mass classification Mammogram Support vector machine Neural network Bayesian classifier Support vector machines Training Wavelet Accuracy Databases Feature extraction Radial and circular features Cancer
Content Type	Text
Resource Type	Article

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