Log-gabor wavelets based breast carcinoma classification using least square support vector machine

Breast cancer diagnosis can be done through the pathologic assessments of breast tissue samples such as core needle biopsy technique. Testing for detection of this cancer involves visual microscopic test of breast tissue samples. The result of analysis on this sample by pathologist is crucial for breast cancer patient. In this paper, nucleus of core needle biopsy samples are investigated after decomposition by means of the log-gabor wavelet transform and a novel method is developed to compute the complex color wavelet features based on the color textural information. These color textural features are used for breast cancer diagnosis using least square support vector machine (LS-SVM) classifier algorithm. The ability of properly trained least square support vector machine (LS-SVM) is to correctly classify patterns makes them particularly suitable for use in an expert system that aids in the diagnosis of cancer tissue samples. The overall accuracy of the proposed using LS-SVM classifier shows better result, which will be useful for automation in cancer diagnosis.