SVM and Neural Networks comparison in mammographic CAD

Author

Garcia-Orellana, C.J. ; Gallardo-Caballero, R. ; Macias-Macias, M. ; Gonzalez-Velasco, H.

Author_Institution

Univ. de Extremadura, Badajoz

fYear

2007

fDate

22-26 Aug. 2007

Firstpage

3204

Lastpage

3207

Abstract

The purpose of this work is to compare the performance of support vector machines (SVM) and multi-layer perceptron (MLP) in the task of detection and diagnosis of microcalcification clusters in mammograms (MCCs). As data source, the "digital database for screening mammography"; (DDSM) was used. The results show a similar performance for SVM and MLP, in both tasks, detection and diagnosis (slightly better for MLP in detection).

Keywords

biological organs; cancer; mammography; medical image processing; multilayer perceptrons; support vector machines; visual databases; breast cancer; digital database; mammographic CAD; microcalcification cluster detection; microcalcification cluster diagnosis; multilayer perceptron; neural networks; screening mammography; support vector machines; Breast cancer; Cancer detection; Coronary arteriosclerosis; Independent component analysis; Lesions; Mammography; Neural networks; Proposals; Support vector machines; Tumors; Algorithms; Breast Diseases; Calcinosis; Expert Systems; Female; Humans; Mammography; Neural Networks (Computer); Pattern Recognition, Automated; Radiographic Image Enhancement; Radiographic Image Interpretation, Computer-Assisted; Reproducibility of Results; Sensitivity and Specificity;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE

Conference_Location

Lyon

ISSN

1557-170X

Print_ISBN

978-1-4244-0787-3

Type

conf

DOI

10.1109/IEMBS.2007.4353011

Filename

4353011