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Proceedings Paper

Case base classification on digital mammograms: improving the performance of case base classifier
Author(s): Valliappan Raman; H. H. Then; Putra Sumari; N. Venkatesa Mohan
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Paper Abstract

Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. The aim of the research presented here is in twofold. First stage of research involves machine learning techniques, which segments and extracts features from the mass of digital mammograms. Second level is on problem solving approach which includes classification of mass by performance based case base classifier. In this paper we build a case-based Classifier in order to diagnose mammographic images. We explain different methods and behaviors that have been added to the classifier to improve the performance of the classifier. Currently the initial Performance base Classifier with Bagging is proposed in the paper and it's been implemented and it shows an improvement in specificity and sensitivity.

Paper Details

Date Published: 30 September 2011
PDF: 7 pages
Proc. SPIE 8285, International Conference on Graphic and Image Processing (ICGIP 2011), 828506 (30 September 2011); doi: 10.1117/12.913026
Show Author Affiliations
Valliappan Raman, Swinburne Univ. of Technology (Malaysia)
H. H. Then, Swinburne Univ. of Technology (Malaysia)
Putra Sumari, Univ. Sains Malaysia Penang (Malaysia)
N. Venkatesa Mohan, Shri Krishna's Specialty Hospital (India)


Published in SPIE Proceedings Vol. 8285:
International Conference on Graphic and Image Processing (ICGIP 2011)
Yi Xie; Yanjun Zheng, Editor(s)

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