Diagnosis of breast cancer tumor based on manifold learning and Support Vector Machine

This paper proposes an efficient algorithm based on manifold learning and support vector machine (SVM) for the diagnosis of breast cancer tumor. First, Isomap algorithm is implemented to project high-dimensional breast tumor data to much lower dimensional space, then the processed data are classified by the SVM. Experimental and analytical results show that in the diagnosis of breast cancer tumor the proposed method can greatly speed up the training and testing of the classifier and get high testing correct rate, superior to the classical principal component analysis (PCA) algorithm.

[1]  Robert M. Nishikawa,et al.  A study on several Machine-learning methods for classification of Malignant and benign clustered microcalcifications , 2005, IEEE Transactions on Medical Imaging.

[2]  S. Setarehdan,et al.  Manifold Learning Applied on EEG Signal of the Epileptic Patients for Detection of Normal and Pre-Seizure States , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[3]  Olvi L. Mangasarian,et al.  Nuclear feature extraction for breast tumor diagnosis , 1993, Electronic Imaging.

[4]  Bernhard Schölkopf,et al.  Nonlinear Component Analysis as a Kernel Eigenvalue Problem , 1998, Neural Computation.

[5]  An Approach Based on Immune Algorithm and SVM for Detection and Classification of Microcalcifications , 2007, 2007 1st International Conference on Bioinformatics and Biomedical Engineering.

[6]  Nello Cristianini,et al.  Kernel Methods for Pattern Analysis , 2004 .

[7]  Mikhail Belkin,et al.  Laplacian Eigenmaps for Dimensionality Reduction and Data Representation , 2003, Neural Computation.

[8]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[9]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[10]  W. N. Street,et al.  Breast cytology diagnosis with digital image analysis. , 1993, Analytical and quantitative cytology and histology.

[11]  J. Listgarten,et al.  Predictive Models for Breast Cancer Susceptibility from Multiple Single Nucleotide Polymorphisms , 2004, Clinical Cancer Research.

[12]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.