Breast cancer detection from FNA using SVM with different parameter tuning systems and SOM

Breast cancer detection from FNA using SVM with different parameter tuning systems and SOM–RBF classifier

Published:2007-05 Issue:3-4 Volume:344 Page:285-311
ISSN:0016-0032
Container-title:Journal of the Franklin Institute
language:en
Short-container-title:Journal of the Franklin Institute

Author:

Mu Tingting,Nandi Asoke K.

Publisher

Elsevier BV

Subject

Applied Mathematics,Computer Networks and Communications,Signal Processing,Control and Systems Engineering

Reference40 articles.

1. Mammography screening in the Netherlands: delay in the diagnosis of breast cancer after breast cancer screening;Duijm;Br. J. Cancer,2004

2. Breast Cancer Diagnostic Algorithms for Primary Care Providers, Breast Expert Workgroup, third ed., Cancer Detection Section, California Department of Health Service, 2005.

3. Breast cytology diagnosis via digital image analysis;Wolberg;Anal. Quant. Cytol. Histol.,1993

4. Machine learning techniques to diagnose breast cancer from fine-needle aspirates;Wolberg;Cancer Lett.,1994

5. Image analysis and machine learning applied to breast cancer diagnosis and prognosis;Wolberg;Anal. Quant. Cytol. Histol.,1995

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