Journal Paper

Paper Title - Gabor Wavelet Based Features For Mammogram Analysis And Classification Of Breast Cancer


Abstract
Breast cancer is the most commonly occurring cancer in women. Early detection of breast cancer is important step in diagnosis of the abnormalities which may reduce the mortality rate. It can be achieved using digital mammography. Digital mammography has become the most effective techniques for the early detection of breast cancer. . Mammography is most reliable and widespread method for early detection of breast cancer. This system includes Preprocessing on mammogram image and uses wavelet feature extraction to improve sensitivity. It involves three major steps-Preprocessing, Feature Extraction and Classification. Gabor wavelets based features are extracted from medical mammogram images representing normal tissues or benign and malign tumors. After completing preprocessing, Principal Component Analysis (PCA) is used to extract feature vectors of all images in the database. PCA is a useful statistical technique that has found application in fields such as face recognition and image compression, and is a common technique for finding patterns in data of high dimension. These extracted features are used for classification of mammogram into malignant and benign. The SVM classifier is used for classification. Index Terms— Gabor Wavelet, Mammogram images, Principal Component Analysis , Suport Vector Machine.


Author - Snehal a. Mane, k. V. Kulhalli

Citation - Snehal a. Mane   ,   k. V. Kulhalli   ,   Snehal a. Mane, k. V. Kulhalli " Gabor Wavelet Based Features For Mammogram Analysis And Classification Of Breast Cancer " , International Journal of Industrial Electronics and Electrical Engineering , Volume-3,Issue-12  ( Dec, 2015 )

Indexed - Google Scholar


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| Published on 2015-12-29