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

• Published in: Proceedings 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
• Language: English
• 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
• ISBN: 9780769529998; 0769529992
• DOI: 10.1109/FBIT.2007.9

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.