Computer-Aided Diagnosis of Cross-Institutional Mammograms Using Support Vector Machines with Feature Elimination

In the analysis of digital or digitized mammographic images, a requirement is to learn to separate benign calcifications from malignant ones. Such an activity could form part of a computer-aided diagnosis (CAD) tool. We present a CAD study of calcification lesions to demonstrate that CAD of same-ins...

Full description

Saved in:
Bibliographic Details
Published inProceedings of the Frontiers in the Convergence of Bioscience and Information Technolgies : Jeju Island, Korea, October 11-13, 2007 pp. 396 - 402
Main Authors Saejoon Kim, Sejong Yoon, Donghyuk Shin
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2007
Subjects
Cancer
Classification algorithms
Computer aided diagnosis
Delta-sigma modulation
Lesions
Mammography
Support vector machine classification
Support vector machines
System testing
Online AccessGet full text
ISBN9780769529998
0769529992
DOI10.1109/FBIT.2007.9

Cover

More Information
Summary:In the analysis of digital or digitized mammographic images, a requirement is to learn to separate benign calcifications from malignant ones. Such an activity could form part of a computer-aided diagnosis (CAD) tool. We present a CAD study of calcification lesions to demonstrate that CAD of same-institutional mammograms provides significantly higher accuracy compared to that of cross-institutional mammograms. Moreover, using only a subset of the widely used six BI-RADS features together with patient age and subtlety value describing each calcification lesion is shown to increase the accuracy of CAD.
ISBN:9780769529998
0769529992
DOI:10.1109/FBIT.2007.9