Abstract：
:Breast cancer diagnosis can be done through the pathologic assessments of breast tissue samples such as core needle biopsy technique. The result of analysis on this sample by pathologist is crucial for breast cancer patient. In this paper, nucleus of tissue samples are investigated after decomposition by means of the Log-Gabor wavelet on HSV color domain and an algorithm is developed to compute the color wavelet features. These features are used for breast cancer diagnosis using Support Vector Machine (SVM) classifier algorithm. The ability of properly trained SVM is to correctly classify patterns and make them particularly suitable for use in an expert system that aids in the diagnosis of cancer tissue samples. The results are compared with other multivariate classifiers such as Naïves Bayes classifier and Artificial Neural Network. The overall accuracy of the proposed method using SVM classifier will be further useful for automation in cancer diagnosis.
journal_name
J Med Systjournal_title
Journal of medical systemsauthors
Issac Niwas S,Palanisamy P,Chibbar R,Zhang WJdoi
10.1007/s10916-011-9788-9subject
Has Abstractpub_date
2012-10-01 00:00:00pages
3091-102issue
5eissn
0148-5598issn
1573-689Xjournal_volume
36pub_type
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